Last data update: May 20, 2024. (Total: 46824 publications since 2009)
Records 1-30 (of 224 Records) |
Query Trace: Richard R[original query] |
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Cost effectiveness and decision analysis for national airport screening options to reduce risk of COVID-19 introduction in Uganda, 2020
Amanya G , Washington ML , Kadobera D , Richard M , Ndyabakiira A , Harris J . Cost Eff Resour Alloc 2024 22 (1) 40 INTRODUCTION: Early during the COVID-19 outbreak, various approaches were utilized to prevent COVID-19 introductions from incoming airport travellers. However, the costs and effectiveness of airport-specific interventions have not been evaluated. METHODS: We evaluated policy options for COVID-19-specific interventions at Entebbe International Airport for costs and impact on COVID-19 case counts, we took the government payer perspective. Policy options included; (1)no screening, testing, or mandatory quarantine for any incoming traveller; (2)mandatory symptom screening for all incoming travellers with RT-PCR testing only for the symptomatic and isolation of positives; and (3)mandatory 14-day quarantine and one-time testing for all, with 10-day isolation of persons testing positive. We calculated incremental cost-effectiveness ratios (ICERs) in US$ per additional case averted. RESULTS: Expected costs per incoming traveller were $0 (Option 1), $19 (Option 2), and $766 (Option 3). ICERs per case averted were $257 for Option 2 (which averted 4,948 cases), and $10,139 for Option 3 (which averted 5,097 cases) compared with Option I. Two-week costs were $0 for Option 1, $1,271,431 Option 2, and $51,684,999 Option 3. The per-case ICER decreased with increase in prevalence. The cost-effectiveness of our interventions was modestly sensitive to the prevalence of COVID-19, diagnostic test sensitivity, and testing costs. CONCLUSION: Screening all incoming travellers, testing symptomatic persons, and isolating positives (Option 2) was the most cost-effective option. A higher COVID-19 prevalence among incoming travellers increased cost-effectiveness of airport-specific interventions. This model could be used to evaluate prevention options at the airport for COVID-19 and other infectious diseases with similar requirements for control. |
Experience and findings from surveillance peer review in Nigeria, August 2017-May 2019
Hamisu AW , Etapelong SG , Ayodeji I , Richard B , Fiona B , Gidado S , Abbott SL , Edukugho AA , Bolu O , Adeyelu A , Mawashi KY , Adamu US , Nsubuga P , Shuaib F . Pan Afr Med J 2023 45 9 INTRODUCTION: acute flaccid paralysis (AFP) surveillance is the gold standard of the Global Polio Eradication Initiative (GPEI) for detecting cases of poliomyelitis and tracking poliovirus transmission. Nigeria's AFP surveillance performance indicators are among the highest in countries of the World Health Organization (WHO) African Region. The primary AFP surveillance performance indicators are the rate of non-polio AFP among children and the proportion of timely, adequate specimen collection. The surveillance working group of the National Emergency Operations Centre assessed the quality of AFP surveillance data in some reportedly high-performing states. METHODS: we conducted a retrospective review of AFP surveillance performance indicators in Nigeria for 2010-2019. We also reviewed data in reports from four groups of surveillance peer reviews and validation visits (conducted by in-country GPEI partners) during August 2017-May 2019 in 16 states with high primary AFP surveillance indicators; the validation visits reviewed clinical information and the dates of specimen collection and onset of paralysis with caretakers. RESULTS: there were consistently increasing AFP surveillance primary performance indicators during 2010-2016, followed by declines during 2017-2019. From the data for 16 states with peer reviews conducted from August 2017-May 2019, overall concordance of reported and "true" (validated) AFP indicator data in peer review investigations was highly variable. True AFP concordance ranged from 58%-100%, and stool timeliness concordance ranged from 56%-95%. The most common clinical causes of reported AFP cases that were not true AFP were spastic paralysis, malaria, sickle cell disease, and malnutrition. All the states that participated in peer reviews developed surveillance improvement plans based on the gaps identified. CONCLUSION: Nigeria has highly sensitive AFP surveillance according to reported primary AFP performance indicators. The findings of peer reviews indicate that the AFP surveillance system needs to be strengthened and well-supervised to enhance data quality. |
Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition.
Pop M , Walker AW , Paulson J , Lindsay B , Antonio M , Hossain MA , Oundo J , Tamboura B , Mai V , Astrovskaya I , Corrada Bravo H , Rance R , Stares M , Levine MM , Panchalingam S , Kotloff K , Ikumapayi UN , Ebruke C , Adeyemi M , Ahmed D , Ahmed F , Alam MT , Amin R , Siddiqui S , Ochieng JB , Ouma E , Juma J , Mailu E , Omore R , Morris JG , Breiman RF , Saha D , Parkhill J , Nataro JP , Stine OC . Genome Biol 2014 15 (6) R76 BACKGROUND: Diarrheal diseases continue to contribute significantly to morbidity and mortality in infants and young children in developing countries. There is an urgent need to better understand the contributions of novel, potentially uncultured, diarrheal pathogens to severe diarrheal disease, as well as distortions in normal gut microbiota composition that might facilitate severe disease. RESULTS: We use high throughput 16S rRNA gene sequencing to compare fecal microbiota composition in children under five years of age who have been diagnosed with moderate to severe diarrhea (MSD) with the microbiota from diarrhea-free controls. Our study includes 992 children from four low-income countries in West and East Africa, and Southeast Asia. Known pathogens, as well as bacteria currently not considered as important diarrhea-causing pathogens, are positively associated with MSD, and these include Escherichia/Shigella, and Granulicatella species, and Streptococcus mitis/pneumoniae groups. In both cases and controls, there tend to be distinct negative correlations between facultative anaerobic lineages and obligate anaerobic lineages. Overall genus-level microbiota composition exhibit a shift in controls from low to high levels of Prevotella and in MSD cases from high to low levels of Escherichia/Shigella in younger versus older children; however, there was significant variation among many genera by both site and age. CONCLUSIONS: Our findings expand the current understanding of microbiota-associated diarrhea pathogenicity in young children from developing countries. Our findings are necessarily based on correlative analyses and must be further validated through epidemiological and molecular techniques. |
A standardised method for interpreting the association between mutations and phenotypic drug resistance in Mycobacterium tuberculosis.
Miotto P , Tessema B , Tagliani E , Chindelevitch L , Starks AM , Emerson C , Hanna D , Kim PS , Liwski R , Zignol M , Gilpin C , Niemann S , Denkinger CM , Fleming J , Warren RM , Crook D , Posey J , Gagneux S , Hoffner S , Rodrigues C , Comas I , Engelthaler DM , Murray M , Alland D , Rigouts L , Lange C , Dheda K , Hasan R , Ranganathan UDK , McNerney R , Ezewudo M , Cirillo DM , Schito M , Köser CU , Rodwell TC . Eur Respir J 2017 50 (6) A clear understanding of the genetic basis of antibiotic resistance in Mycobacterium tuberculosis is required to accelerate the development of rapid drug susceptibility testing methods based on genetic sequence.Raw genotype-phenotype correlation data were extracted as part of a comprehensive systematic review to develop a standardised analytical approach for interpreting resistance associated mutations for rifampicin, isoniazid, ofloxacin/levofloxacin, moxifloxacin, amikacin, kanamycin, capreomycin, streptomycin, ethionamide/prothionamide and pyrazinamide. Mutation frequencies in resistant and susceptible isolates were calculated, together with novel statistical measures to classify mutations as high, moderate, minimal or indeterminate confidence for predicting resistance.We identified 286 confidence-graded mutations associated with resistance. Compared to phenotypic methods, sensitivity (95% CI) for rifampicin was 90.3% (89.6-90.9%), while for isoniazid it was 78.2% (77.4-79.0%) and their specificities were 96.3% (95.7-96.8%) and 94.4% (93.1-95.5%), respectively. For second-line drugs, sensitivity varied from 67.4% (64.1-70.6%) for capreomycin to 88.2% (85.1-90.9%) for moxifloxacin, with specificity ranging from 90.0% (87.1-92.5%) for moxifloxacin to 99.5% (99.0-99.8%) for amikacin.This study provides a standardised and comprehensive approach for the interpretation of mutations as predictors of M. tuberculosis drug-resistant phenotypes. These data have implications for the clinical interpretation of molecular diagnostics and next-generation sequencing as well as efficient individualised therapy for patients with drug-resistant tuberculosis. |
What's next: using infectious disease mathematical modelling to address health disparities
Richard DM , Lipsitch M . Int J Epidemiol 2023 Before and during the COVID-19 pandemic, an individual’s age and race/ethnicity have been highly predictive of their risk of infectious diseases and their health consequences. Disparities were evidenced in COVID-19 incidence rates and in hospitalization, severity and mortality metrics in the USA1 and in other countries.2,3 Identifying these disparate outcomes associated with demographic variables is valuable mainly if it prompts investigation into what mechanisms generate the disparities and inform how they can be reduced.4 A prominent report from the UK succinctly outlined that social determinants such as occupation, household characteristics, surrounding population density, urbanicity and social deprivation were all associated with increased risk of COVID-19 infection.3 Others have noted that social determinants can play a role in all stages of an outbreak, providing pathways for unequal exposure, transmission, susceptibility and treatment that produce and escalate disparities in health outcomes.5 |
Performance of CHROMagar ESBL media for the surveillance of extended-spectrum cephalosporin-resistant Enterobacterales (ESCrE) from rectal swabs in Botswana
Mannathoko N , Lautenbach E , Mosepele M , Otukile D , Sewawa K , Glaser L , Cressman L , Cowden L , Alby K , Jaskowiak-Barr A , Gross R , Mokomane M , Paganotti GM , Styczynski A , Smith RM , Snitkin E , Wan T , Bilker WB , Richard-Greenblatt M . J Med Microbiol 2023 72 (11) Introduction. Lack of laboratory capacity hampers consistent national antimicrobial resistance (AMR) surveillance. Chromogenic media may provide a practical screening tool for detection of individuals colonized by extended-spectrum beta-lactamase (ESBL)-producing organisms.Hypothesis. CHROMagar ESBL media represent an adequate screening method for the detection of extended-spectrum cephalosporin-resistant Enterobacterales (ESCrE), isolated from rectal swabs.Aim. To evaluate the performance of CHROMagar ESBL media to accurately identify ESCrE isolates from rectal swab samples attained from hospitalized and community participants.Methodology. All participants provided informed consent prior to enrolment. Rectal swabs from 2469 hospital and community participants were inoculated onto CHROMagar ESBL. The performance of CHROMagar ESBL to differentiate Escherichia coli and Klebsiella spp., Enterobacter spp. and Citrobacter spp. (KEC spp.) as well as select for extended-spectrum cephalosporin resistance were compared to matrix-assisted laser desorption/ionization-time-of-flight MS (MALDI-TOF-MS) and VITEK-2 automated susceptibility testing.Results. CHROMagar ESBL had a positive and negative agreement of 91.2 % (95 % CI, 88.4-93.3) and 86.8 % (95 % CI, 82.0-90.7) for E. coli and 88.1 % (95 % CI 83.2-92.1) and 87.6 % (95 % CI 84.7-90.2) for KEC spp. differentiation, respectively, when compared to species ID by MALDI-TOF-MS. When evaluated for phenotypic susceptibilities (VITEK-2), 88.1 % (714/810) of the isolates recovered on the selective agar exhibited resistance to third-generation cephalosporins.Conclusion. The performance characteristics of CHROMagar ESBL media suggest that they may be a viable screening tool for the identification of ESCrE from hospitalized and community participants and could be used to inform infection prevention and control practices in Botswana and potentially other low-and middle-income countries (LMICs). Further studies are required to analyse the costs and the impact on time-to-result of the media in comparison with available laboratory methods for ESCrE surveillance in the country. |
The United States COVID-19 Forecast Hub dataset (preprint)
Cramer EY , Huang Y , Wang Y , Ray EL , Cornell M , Bracher J , Brennen A , Rivadeneira AJC , Gerding A , House K , Jayawardena D , Kanji AH , Khandelwal A , Le K , Mody V , Mody V , Niemi J , Stark A , Shah A , Wattanchit N , Zorn MW , Reich NG , US COVID-19 Forecast Hub Consortium , Lopez VK , Walker JW , Slayton RB , Johansson MA , Biggerstaff M . medRxiv 2021 2021.11.04.21265886 Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident hospitalizations, incident cases, incident deaths, and cumulative deaths due to COVID-19 at national, state, and county levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work. Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below: AIpert-pwllnod: Natural Sciences and Engineering Research Council of Canada; Caltech-CS156: Gary Clinard Innovation Fund; CEID-Walk: University of Georgia; CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook; COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health; Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information & Data Science Pilot Project; Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation; CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation; DDS-NBDS: NSF III-1812699; epiforecasts-ensemble1: Wellcome Trust (210758/Z/18/Z) FDANIHASU: supported by the Intramural Research Program of the NIH/NIDDK; GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowment, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines, CDC MInD-Healthcare U01CK000531-Supplement; IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096); Imperial-ensemble1: SB acknowledges funding from the Wellcome Trust (219415); Institute of Business Forecasting: IBF; IowaStateLW-STEM: NSF DMS-1916204, Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics; IUPUI CIS: NSF; JHU_CSSE-DECOM: JHU CSSE: National Science Foundation (NSF) RAPID Real-time Forecasting of COVID-19 risk in the USA. 2021-2022. Award ID: 2108526. National Science Foundation (NSF) RAPID Development of an interactive web-based dashboard to track COVID-19 in real-time. 2020. Award ID: 2028604; JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers for Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant); JHU_UNC_GAS-StatMechP ol: NIH NIGMS: R01GM140564; JHUAPL-Bucky: US Dept of Health and Human Services; KITmetricslab-select_ensemble: Daniel Wolffram gratefully acknowledges support by the Klaus Tschira Foundation; LANL-GrowthRate: LANL LDRD 20200700ER; MIT-Cassandra: MIT Quest for Intelligence; MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01; CA NU38OT000297 from the Council of State and Territorial Epidemiologists (CSTE); NotreDame-FRED: NSF RAPID DEB 2027718; NotreDame-mobility: NSF RAPID DEB 2027718; PSI-DRAFT: NSF RAPID Grant # 2031536; QJHong-Encounter: NSF DMR-2001411 and DMR-1835939; SDSC_ISG-TrendModel: The development of the dashboard was partly funded by the Fondation Privee des Hopitaux Universitaires de Geneve; UA-EpiCovDA: NSF RAPID Grant # 2028401; UChicagoCHATTOPADHYAY-UnIT: Defense Advanced Research Projects Agency (DARPA) #HR00111890043/P00004 (I. Chattopadhyay, University of Chicago); UCSB-ACTS: NSF RAPID IIS 2029626; UCSD_NEU-DeepGLEAM: Google Faculty Award, W31P4Q-21-C-0014; UMass-MechBayes: NIGMS #R35GM119582, NSF #1749854, NIGMS #R35GM119582; UMich-RidgeTfReg: This project is funded by the University of Michigan Physics Department and the University of Michigan Office of Research; UVA-Ensemble: National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and Virginia Dept of Health Grant VDH-21-501-0141; Wadnwani_AI-BayesOpt: This study is made possible by the generous support of the American People through the United States Agency for International Development (USAID). The work described in this article was implemented under the TRACETB Project, managed by WIAI under the terms of Cooperative Agreement Number 72038620CA00006. The contents of this manuscript are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government; WalmartLabsML-LogForecasting: Team acknowledges Walmart to support this study Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data produced are available online at https://github.com/reichlab/covid19-forecast-hub https://github.com/reichlab/covid19-forecast-hub |
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US (preprint)
Cramer EY , Ray EL , Lopez VK , Bracher J , Brennen A , Castro Rivadeneira AJ , Gerding A , Gneiting T , House KH , Huang Y , Jayawardena D , Kanji AH , Khandelwal A , Le K , Mühlemann A , Niemi J , Shah A , Stark A , Wang Y , Wattanachit N , Zorn MW , Gu Y , Jain S , Bannur N , Deva A , Kulkarni M , Merugu S , Raval A , Shingi S , Tiwari A , White J , Abernethy NF , Woody S , Dahan M , Fox S , Gaither K , Lachmann M , Meyers LA , Scott JG , Tec M , Srivastava A , George GE , Cegan JC , Dettwiller ID , England WP , Farthing MW , Hunter RH , Lafferty B , Linkov I , Mayo ML , Parno MD , Rowland MA , Trump BD , Zhang-James Y , Chen S , Faraone SV , Hess J , Morley CP , Salekin A , Wang D , Corsetti SM , Baer TM , Eisenberg MC , Falb K , Huang Y , Martin ET , McCauley E , Myers RL , Schwarz T , Sheldon D , Gibson GC , Yu R , Gao L , Ma Y , Wu D , Yan X , Jin X , Wang YX , Chen Y , Guo L , Zhao Y , Gu Q , Chen J , Wang L , Xu P , Zhang W , Zou D , Biegel H , Lega J , McConnell S , Nagraj VP , Guertin SL , Hulme-Lowe C , Turner SD , Shi Y , Ban X , Walraven R , Hong QJ , Kong S , van de Walle A , Turtle JA , Ben-Nun M , Riley S , Riley P , Koyluoglu U , DesRoches D , Forli P , Hamory B , Kyriakides C , Leis H , Milliken J , Moloney M , Morgan J , Nirgudkar N , Ozcan G , Piwonka N , Ravi M , Schrader C , Shakhnovich E , Siegel D , Spatz R , Stiefeling C , Wilkinson B , Wong A , Cavany S , España G , Moore S , Oidtman R , Perkins A , Kraus D , Kraus A , Gao Z , Bian J , Cao W , Lavista Ferres J , Li C , Liu TY , Xie X , Zhang S , Zheng S , Vespignani A , Chinazzi M , Davis JT , Mu K , Pastore YPiontti A , Xiong X , Zheng A , Baek J , Farias V , Georgescu A , Levi R , Sinha D , Wilde J , Perakis G , Bennouna MA , Nze-Ndong D , Singhvi D , Spantidakis I , Thayaparan L , Tsiourvas A , Sarker A , Jadbabaie A , Shah D , Della Penna N , Celi LA , Sundar S , Wolfinger R , Osthus D , Castro L , Fairchild G , Michaud I , Karlen D , Kinsey M , Mullany LC , Rainwater-Lovett K , Shin L , Tallaksen K , Wilson S , Lee EC , Dent J , Grantz KH , Hill AL , Kaminsky J , Kaminsky K , Keegan LT , Lauer SA , Lemaitre JC , Lessler J , Meredith HR , Perez-Saez J , Shah S , Smith CP , Truelove SA , Wills J , Marshall M , Gardner L , Nixon K , Burant JC , Wang L , Gao L , Gu Z , Kim M , Li X , Wang G , Wang Y , Yu S , Reiner RC , Barber R , Gakidou E , Hay SI , Lim S , Murray C , Pigott D , Gurung HL , Baccam P , Stage SA , Suchoski BT , Prakash BA , Adhikari B , Cui J , Rodríguez A , Tabassum A , Xie J , Keskinocak P , Asplund J , Baxter A , Oruc BE , Serban N , Arik SO , Dusenberry M , Epshteyn A , Kanal E , Le LT , Li CL , Pfister T , Sava D , Sinha R , Tsai T , Yoder N , Yoon J , Zhang L , Abbott S , Bosse NI , Funk S , Hellewell J , Meakin SR , Sherratt K , Zhou M , Kalantari R , Yamana TK , Pei S , Shaman J , Li ML , Bertsimas D , Skali Lami O , Soni S , Tazi Bouardi H , Ayer T , Adee M , Chhatwal J , Dalgic OO , Ladd MA , Linas BP , Mueller P , Xiao J , Wang Y , Wang Q , Xie S , Zeng D , Green A , Bien J , Brooks L , Hu AJ , Jahja M , McDonald D , Narasimhan B , Politsch C , Rajanala S , Rumack A , Simon N , Tibshirani RJ , Tibshirani R , Ventura V , Wasserman L , O'Dea EB , Drake JM , Pagano R , Tran QT , Ho LST , Huynh H , Walker JW , Slayton RB , Johansson MA , Biggerstaff M , Reich NG . medRxiv 2021 2021.02.03.21250974 Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naïve baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work.Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below. CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook. CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation. COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health. Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information& Data Science Pilot Project. Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation. DDS-NBDS: NSF III-1812699. EPIFORECASTS-ENSEMBLE1: Wellcome Trust (210758/Z/18/Z) GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowments, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. & Charlene Zalesky, and Claudia & Paul Raines GT-DeepCOVID: CDC MInD-Healthcare U01CK000531-Supplement. NSF (Expeditions CCF-1918770, CAREER IIS-2028586, RAPID IIS-2027862, Medium IIS-1955883, NRT DGE-1545362), CDC MInD program, ORNL and funds/computing resources from Georgia Tech and GTRI. IHME: This work was supported by the Bill & Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096). IowaStateLW-STEM: Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1916204, NSF CCF-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics. JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, US Office of Foreign Disaster Assistance, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers fo Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, & Infectious Disease Initiative (26798 Seed Grant). LANL-GrowthRate: LANL LDRD 20200700ER. MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01. NotreDame-mobility and NotreDame-FRED: NSF RAPID DEB 2027718 UA-EpiCovDA: NSF RAPID Grant # 2028401. UCSB-ACTS: NSF RAPID IIS 2029626. UCSD-NEU: Google Faculty Award, DARPA W31P4Q-21-C-0014, COVID Supplement CDC-HHS-6U01IP001137-01. UMass-MechBayes: NIGMS R35GM119582, NSF 1749854. UMich-RidgeTfReg: The University of Michigan Physics Department and the University of Michigan Office of Research.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:UMass-Amherst IRBAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data and code referred to in the manuscript are publicly available. https://github.com/reichlab/covid19-forecast-hub/ https://github.com/reichlab/covidEnsembles https://zoltardata.com/project/44 |
Risk factors for community colonization with extended-spectrum cephalosporin-resistant enterobacterales (escre) in Botswana: An Antibiotic Resistance in Communities and Hospitals (ARCH) Study
Lautenbach E , Mosepele M , Smith RM , Styczynski A , Gross R , Cressman L , Jaskowiak-Barr A , Alby K , Glaser L , Richard-Greenblatt M , Cowden L , Sewawa K , Otukile D , Paganotti GM , Mokomane M , Bilker WB , Mannathoko N . Clin Infect Dis 2023 77 S89-s96 BACKGROUND: The epidemiology of extended-spectrum cephalosporin-resistant Enterobacterales (ESCrE) in low- and middle-income countries (LMICs) is poorly described. Identifying risk factors for ESCrE colonization is critical to inform antibiotic resistance reduction strategies because colonization is typically a precursor to infection. METHODS: From 15 January 2020 to 4 September 2020, we surveyed a random sample of clinic patients at 6 sites in Botswana. We also invited each enrolled participant to refer up to 3 adults and children. All participants had rectal swabs collected that were inoculated onto chromogenic media followed by confirmatory testing. Data were collected on demographics, comorbidities, antibiotic use, healthcare exposures, travel, and farm and animal contact. Participants with ESCrE colonization (cases) were compared with noncolonized participants (controls) to identify risk factors for ESCrE colonization using bivariable, stratified, and multivariable analyses. RESULTS: A total of 2000 participants were enrolled. There were 959 (48.0%) clinic participants, 477 (23.9%) adult community participants, and 564 (28.2%) child community participants. The median (interquartile range) age was 30 (12-41) and 1463 (73%) were women. There were 555 cases and 1445 controls (ie, 27.8% of participants were ESCrE colonized). Independent risk factors (adjusted odds ratio [95% confidence interval]) for ESCrE included healthcare exposure (1.37 [1.08-1.73]), foreign travel [1.98 (1.04-3.77]), tending livestock (1.34 [1.03-1.73]), and presence of an ESCrE-colonized household member (1.57 [1.08-2.27]). CONCLUSIONS: Our results suggest healthcare exposure may be important in driving ESCrE. The strong links to livestock exposure and household member ESCrE colonization highlight the potential role of common exposure or household transmission. These findings are critical to inform strategies to curb further emergence of ESCrE in LMICs. |
A test of the predictive validity of relative versus absolute income for self-reported health and well-being in the United States
Brady D , Curran M , Carpiano RM . Demogr Res 2023 48 775-808 BACKGROUND A classic debate concerns whether absolute or relative income is more salient. Absolute values resources as constant across time and place while relative contextualizes one’s hierarchical location in the distribution of a time and place. OBJECTIVE This study investigates specifically whether absolute income or relative income matters more for health and well-being. METHODS We exploit within-person, within-age, and within-time variation with higher-quality income measures and multiple health and well-being outcomes in the United States. Using the Panel Study of Income Dynamics and the Cross-National Equivalent File, we estimate three-way fixed effects models of self-rated health, poor health, psychological distress, and life satisfaction. RESULTS For all four outcomes, relative income has much larger standardized coefficients than absolute income. Robustly, the confidence intervals for relative income do not overlap with zero. By contrast, absolute income mostly has confidence intervals that overlap with zero, and its coefficient is occasionally signed in the wrong direction. A variety of robustness checks support these results. CONCLUSIONS Relative income has far greater predictive validity than absolute income for self-reported health and well-being. CONTRIBUTION Compared to earlier studies, this study provides a more rigorous comparison and test of the predictive validity of absolute and relative income that is uniquely conducted with data on the United States. This informs debates on income measurement, the sources of health and well-being, and inequalities generally. Plausibly, these results can guide any analysis that includes income in models. © 2023 David Brady, Michaela Curran & Richard M. Carpiano. All Rights Reserved. |
Cryptic transmission of SARS-CoV-2 in Washington State.
Bedford T , Greninger AL , Roychoudhury P , Starita LM , Famulare M , Huang ML , Nalla A , Pepper G , Reinhardt A , Xie H , Shrestha L , Nguyen TN , Adler A , Brandstetter E , Cho S , Giroux D , Han PD , Fay K , Frazar CD , Ilcisin M , Lacombe K , Lee J , Kiavand A , Richardson M , Sibley TR , Truong M , Wolf CR , Nickerson DA , Rieder MJ , Englund JA , Hadfield J , Hodcroft EB , Huddleston J , Moncla LH , Müller NF , Neher RA , Deng X , Gu W , Federman S , Chiu C , Duchin J , Gautom R , Melly G , Hiatt B , Dykema P , Lindquist S , Queen K , Tao Y , Uehara A , Tong S , MacCannell D , Armstrong GL , Baird GS , Chu HY , Shendure J , Jerome KR . medRxiv 2020 Following its emergence in Wuhan, China, in late November or early December 2019, the SARS-CoV-2 virus has rapidly spread throughout the world. On March 11, 2020, the World Health Organization declared Coronavirus Disease 2019 (COVID-19) a pandemic. Genome sequencing of SARS-CoV-2 strains allows for the reconstruction of transmission history connecting these infections. Here, we analyze 346 SARS-CoV-2 genomes from samples collected between 20 February and 15 March 2020 from infected patients in Washington State, USA. We found that the large majority of SARS-CoV-2 infections sampled during this time frame appeared to have derived from a single introduction event into the state in late January or early February 2020 and subsequent local spread, strongly suggesting cryptic spread of COVID-19 during the months of January and February 2020, before active community surveillance was implemented. We estimate a common ancestor of this outbreak clade as occurring between 18 January and 9 February 2020. From genomic data, we estimate an exponential doubling between 2.4 and 5.1 days. These results highlight the need for large-scale community surveillance for SARS-CoV-2 introductions and spread and the power of pathogen genomics to inform epidemiological understanding. |
Ebola Virus Disease Outbreak - Democratic Republic of the Congo, August 2018-November 2019.
Aruna A , Mbala P , Minikulu L , Mukadi D , Bulemfu D , Edidi F , Bulabula J , Tshapenda G , Nsio J , Kitenge R , Mbuyi G , Mwanzembe C , Kombe J , Lubula L , Shako JC , Mossoko M , Mulangu F , Mutombo A , Sana E , Tutu Y , Kabange L , Makengo J , Tshibinkufua F , Ahuka-Mundeke S , Muyembe JJ , Ebola Response CDC , Alarcon Walter , Bonwitt Jesse , Bugli Dante , Bustamante Nirma D , Choi Mary , Dahl Benjamin A , DeCock Kevin , Dismer Amber , Doshi Reena , Dubray Christine , Fitter David , Ghiselli Margherita , Hall Noemi , Hamida Amen Ben , McCollum Andrea M , Neatherlin John , Raghunathan Pratima L , Ravat Fatima , Reynolds Mary G , Rico Adriana , Smith Nailah , Soke Gnakub Norbert , Trudeau Aimee T , Victory Kerton R , Worrell Mary Claire . MMWR Morb Mortal Wkly Rep 2019 68 (50) 1162-1165 On August 1, 2018, the Democratic Republic of the Congo Ministry of Health (DRC MoH) declared the tenth outbreak of Ebola virus disease (Ebola) in DRC, in the North Kivu province in eastern DRC on the border with Uganda, 8 days after another Ebola outbreak was declared over in northwest Équateur province. During mid- to late-July 2018, a cluster of 26 cases of acute hemorrhagic fever, including 20 deaths, was reported in North Kivu province.* Blood specimens from six patients hospitalized in the Mabalako health zone and sent to the Institut National de Recherche Biomédicale (National Biomedical Research Institute) in Kinshasa tested positive for Ebola virus. Genetic sequencing confirmed that the outbreaks in North Kivu and Équateur provinces were unrelated. From North Kivu province, the outbreak spread north to Ituri province, and south to South Kivu province (1). On July 17, 2019, the World Health Organization designated the North Kivu and Ituri outbreak a public health emergency of international concern, based on the geographic spread of the disease to Goma, the capital of North Kivu province, and to Uganda and the challenges to implementing prevention and control measures specific to this region (2). This report describes the outbreak in the North Kivu and Ituri provinces. As of November 17, 2019, a total of 3,296 Ebola cases and 2,196 (67%) deaths were reported, making this the second largest documented outbreak after the 2014-2016 epidemic in West Africa, which resulted in 28,600 cases and 11,325 deaths.(†) Since August 2018, DRC MoH has been collaborating with partners, including the World Health Organization, the United Nations Children's Fund, the United Nations Office for the Coordination of Humanitarian Affairs, the International Organization of Migration, The Alliance for International Medical Action (ALIMA), Médecins Sans Frontières, DRC Red Cross National Society, and CDC, to control the outbreak. Enhanced communication and effective community engagement, timing of interventions during periods of relative stability, and intensive training of local residents to manage response activities with periodic supervision by national and international personnel are needed to end the outbreak. |
Investigation of a COVID-19 outbreak at a regional prison, Northern Uganda, September 2020.
Migisha R , Morukileng J , Biribawa C , Kadobera D , Kisambu J , Bulage L , Ndyabakira A , Katana E , Mills LA , Ario AR , Harris JR . Pan Afri Med J 2022 43 10 Despite implementing measures to prevent introduction of COVID-19 in prisons, a COVID-19 outbreak occurred at Moroto Prison, northern Uganda in September 2020. We investigated factors associated with the introduction and spread of COVID-19 in the prison. A case was PCR-confirmed SARS-CoV-2 infection in a prisoner/staff at Moroto Prison during August-September 2020. We reviewed prison medical records to identify case-patients and interviewed prison and hospital staff to understand possible infection mechanisms for the index case-patient and opportunities for spread. In a retrospective cohort study, we interviewed all prisoners and available staff to identify risk factors. Data were analyzed using log-binomial regression. On September 1, 2020, a recently-hospitalized prisoner with unrecognized SARS-CoV-2 infection was admitted to Moroto Prison quarantine. He had become infected while sharing a hospital ward with a subsequently-diagnosed COVID-19 patient. A sample taken from the hospitalized prisoner on August 20 tested positive on September 3. Mass reactive testing at the prison on September 6, 14, and 15 revealed infection among 202/692 prisoners and 8/90 staff (overall attack rate=27%). One prison staff and one prisoner who cared for the sick prisoner while at the hospital re-entered the main prison without quarantining. Both tested positive on September 6. Food and cleaning service providers also regularly transited between quarantine and unrestricted prison areas. Using facemasks >50% of the time (adjusted risk ratio [aRR]=0.26; 95%CI: 0.13-0.54), or in combination with handwashing after touching surfaces (aRR=0.25; 95%CI: 0.14-0.46) were protective. Prisoners recently transferred from other facilities to Moroto Prison had an increased risk of infection (aRR=1.50; 95%CI: 1.02-2.22). COVID-19 was likely introduced into Moroto Prison quarantine by a prisoner with hospital-acquired infection and delayed test results, and/or by caretakers who were not quarantined after hospital exposures. The outbreak may have amplified via shared food/cleaning service providers who transited between quarantined and non-quarantined prisoners. Facemasks and handwashing were protective. Reduced test turnaround time for the hospitalized prisoner could have averted this outbreak. Testing incoming prisoners for SARS-CoV-2 before quarantine, providing unrestricted soap/water for handwashing, and universal facemask use in prisons could mitigate risk of future outbreaks. © Richard Migisha et al. |
Colonization with extended-spectrum cephalosporin-resistant Enterobacterales (ESCrE) and carbapenem-resistant Enterobacterales (CRE) in healthcare and community settings in Botswana: an antibiotic resistance in communities and hospitals (ARCH) study.
Mannathoko N , Mospele M , Gross R , Smith RM , Alby K , Glaser L , Richard-Greenblatt M , Dumm R , Sharma A , Jaskowiak-Barr A , Cressman L , Sewawa K , Cowden L , Reesey E , Otukile D , Paganotti GM , Mokomane M , Lautenbach E . Int J Infect Dis 2022 122 313-320 OBJECTIVES: Although extended-spectrum cephalosporin-resistant Enterobacterales (ESCrE) and carbapenem-resistant Enterobacterales (CRE) are a global challenge, data on these organisms in low- and middle-income countries are limited. We sought to characterize colonization data critical for larger antibiotic resistance surveillance efforts. METHODS: This study was conducted in three hospitals and six clinics in Botswana. We conducted ongoing surveillance of adult patients in hospitals and clinics as well as adults and children in the community. All participants had rectal swabs obtained for identification of ESCrE and CRE. RESULTS: Enrollment occurred from 1/15/20-9/4/20 but paused from 4/2/20-5/21/20 due to a countrywide COVID-19 lockdown. Of 5,088 individuals approached, 2,469 (49%) participated. ESCrE colonization prevalence was 30.7% overall (43% for hospital participants, 31% for clinic participants, 24% for adult community participants, and 26% for child community participants) (p<0.001). 42 (1.7%) participants were colonized with CRE. CRE colonization prevalence was 1.7% overall (6.8% for hospital participants, 0.7% for clinic participants, 0.2% for adult community participants, and 0.5% for child community participants) (p<0.001). ESCrE and CRE prevalence varied substantially across regions and was significantly higher pre-lockdown vs post-lockdown. CONCLUSIONS: ESCrE colonization was high in all settings in Botswana. CRE prevalence in hospitals was also considerable. Colonization prevalence varied by region and clinical setting and decreased following a countrywide lockdown. |
Development and Evaluation of a Molecular Hepatitis A Virus Assay for Serum and Stool Specimens.
Kozak RA , Rutherford C , Richard-Greenblatt M , Chau NYE , Cabrera A , Biondi M , Borlang J , Day J , Osiowy C , Ramachandran S , Mayer N , Glaser L , Smieja M . Viruses 2022 14 (1) Hepatitis A virus (HAV) is an emerging public health concern and there is an urgent need for ways to rapidly identify cases so that outbreaks can be managed effectively. Conventional testing for HAV relies on anti-HAV IgM seropositivity. However, studies estimate that 10-30% of patients may not be diagnosed by serology. Molecular assays that can directly detect viral nucleic acids have the potential to improve diagnosis, which is key to prevent the spread of infections. In this study, we developed a real-time PCR (RT-PCR) assay to detect HAV RNA for the identification of acute HAV infection. Primers were designed to target the conserved 5'-untranslated region (5'-UTR) of HAV, and the assay was optimized on both the Qiagen Rotor-Gene and the BD MAX. We successfully detected HAV from patient serum and stool samples with moderate differences in sensitivity and specificity depending on the platform used. Our results highlight the clinical utility of using a molecular assay to detect HAV from various specimen types that can be implemented in hospitals to assist with diagnostics, treatment and prevention. |
Transmission Dynamics of Severe Acute Respiratory Syndrome Coronavirus 2 in High-Density Settings, Minnesota, USA, March-June 2020.
Lehnertz NB , Wang X , Garfin J , Taylor J , Zipprich J , VonBank B , Martin K , Eikmeier D , Medus C , Wiedinmyer B , Bernu C , Plumb M , Pung K , Honein MA , Carter R , MacCannell D , Smith KE , Como-Sabetti K , Ehresmann K , Danila R , Lynfield R . Emerg Infect Dis 2021 27 (8) 2052-2063 Coronavirus disease has disproportionately affected persons in congregate settings and high-density workplaces. To determine more about the transmission patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in these settings, we performed whole-genome sequencing and phylogenetic analysis on 319 (14.4%) samples from 2,222 SARS-CoV-2-positive persons associated with 8 outbreaks in Minnesota, USA, during March-June 2020. Sequencing indicated that virus spread in 3 long-term care facilities and 2 correctional facilities was associated with a single genetic sequence and that in a fourth long-term care facility, outbreak cases were associated with 2 distinct sequences. In contrast, cases associated with outbreaks in 2 meat-processing plants were associated with multiple SARS-CoV-2 sequences. These results suggest that a single introduction of SARS-CoV-2 into a facility can result in a widespread outbreak. Early identification and cohorting (segregating) of virus-positive persons in these settings, along with continued vigilance with infection prevention and control measures, is imperative. |
SARS-CoV-2 Variants of Interest and Concern naming scheme conducive for global discourse.
Konings F , Perkins MD , Kuhn JH , Pallen MJ , Alm EJ , Archer BN , Barakat A , Bedford T , Bhiman JN , Caly L , Carter LL , Cullinane A , de Oliveira T , Druce J , El Masry I , Evans R , Gao GF , Gorbalenya AE , Hamblion E , Herring BL , Hodcroft E , Holmes EC , Kakkar M , Khare S , Koopmans MPG , Korber B , Leite J , MacCannell D , Marklewitz M , Maurer-Stroh S , Rico JAM , Munster VJ , Neher R , Munnink BO , Pavlin BI , Peiris M , Poon L , Pybus O , Rambaut A , Resende P , Subissi L , Thiel V , Tong S , van der Werf S , von Gottberg A , Ziebuhr J , Van Kerkhove MD . Nat Microbiol 2021 6 (7) 821-823 A group convened and led by the Virus Evolution Working Group of the World Health Organization reports on its deliberations and announces a naming scheme that will enable clear communication about SARS-CoV-2 variants of interest and concern. | | Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has a linear, unsegmented, positive-sense RNA genome. As with all viruses, SARS-CoV-2 continuously adapts to changing environments in real time via random genome mutations that are subject to natural selection. Most mutations are neutral or detrimental to the virus; however, a small number of mutations may provide a selective advantage, such as escape from the host immune system or resistance to antiviral drugs. Such mutations may also lead to increased fitness for transmissibility. As mutated forms of viruses or variants spread from person to person, they will eventually be detected at the population level. |
Changes to virus taxonomy and to the International Code of Virus Classification and Nomenclature ratified by the International Committee on Taxonomy of Viruses (2021).
Walker PJ , Siddell SG , Lefkowitz EJ , Mushegian AR , Adriaenssens EM , Alfenas-Zerbini P , Davison AJ , Dempsey DM , Dutilh BE , García ML , Harrach B , Harrison RL , Hendrickson RC , Junglen S , Knowles NJ , Krupovic M , Kuhn JH , Lambert AJ , Łobocka M , Nibert ML , Oksanen HM , Orton RJ , Robertson DL , Rubino L , Sabanadzovic S , Simmonds P , Smith DB , Suzuki N , Van Dooerslaer K , Vandamme AM , Varsani A , Zerbini FM . Arch Virol 2021 166 (9) 2633-2648 This article reports the changes to virus taxonomy approved and ratified by the International Committee on Taxonomy of Viruses (ICTV) in March 2021. The entire ICTV was invited to vote on 290 taxonomic proposals approved by the ICTV Executive Committee at its meeting in October 2020, as well as on the proposed revision of the International Code of Virus Classification and Nomenclature (ICVCN). All proposals and the revision were ratified by an absolute majority of the ICTV members. Of note, ICTV mandated a uniform rule for virus species naming, which will follow the binomial 'genus-species' format with or without Latinized species epithets. The Study Groups are requested to convert all previously established species names to the new format. ICTV has also abolished the notion of a type species, i.e., a species chosen to serve as a name-bearing type of a virus genus. The remit of ICTV has been clarified through an official definition of 'virus' and several other types of mobile genetic elements. The ICVCN and ICTV Statutes have been amended to reflect these changes. |
Identification of TB space-time clusters and hotspots in Ouest dpartement, Haiti, 2011-2016
Dismer AM , Charles M , Dear N , Louis-Jean JM , Barthelemy N , Richard M , Morose W , Fitter DL . Public Health Action 2021 11 (2) 101-107 BACKGROUND: Haiti has the highest incidence rate of TB in the Western Hemisphere, with an estimated 170 cases per 100,000 in 2019. Since 2010, control efforts have focused on targeted case-finding activities in urban areas, implementation of rapid molecular diagnostics at high-volume TB centers, and improved reporting. TB analyses are rarely focused on lower geographic units; thus, the major goal was to determine if there were focal areas of TB transmission from 2011 to 2016 at operational geographic levels useful for the National TB Control Program (PNLT). METHODS: We created a geocoder to locate TB cases at the smallest geographic level. Kulldorff's space-time permutation scan, Anselin Moran's I, and Getis-Ord Gi* statistics were used to determine the spatial distribution and clusters of TB. RESULTS: With 91% of cases linked using the geocoder, TB clusters were identified each year. Getis-Ord Gi* analysis revealed 14 distinct spatial clusters of high incidences in the Port-au-Prince metropolitan area. One hundred retrospective space-time clusters were detected. CONCLUSION: Our study confirms the presence of TB hotspots in the Ouest département, with most clusters in the Port-au-Prince metropolitan area. Results will help the PNLT and its partners better design case-finding strategies for these areas. |
Evaluation of post-introduction COVID-19 vaccine effectiveness: Summary of interim guidance of the World Health Organization.
Patel MK , Bergeri I , Bresee JS , Cowling BJ , Crowcroft NS , Fahmy K , Hirve S , Kang G , Katz MA , Lanata CF , L'Azou Jackson M , Joshi S , Lipsitch M , Mwenda JM , Nogareda F , Orenstein WA , Ortiz JR , Pebody R , Schrag SJ , Smith PG , Srikantiah P , Subissi L , Valenciano M , Vaughn DW , Verani JR , Wilder-Smith A , Feikin DR . Vaccine 2021 39 (30) 4013-4024 Phase 3 randomized-controlled trials have provided promising results of COVID-19 vaccine efficacy, ranging from 50 to 95% against symptomatic disease as the primary endpoints, resulting in emergency use authorization/listing for several vaccines. However, given the short duration of follow-up during the clinical trials, strict eligibility criteria, emerging variants of concern, and the changing epidemiology of the pandemic, many questions still remain unanswered regarding vaccine performance. Post-introduction vaccine effectiveness evaluations can help us to understand the vaccine's effect on reducing infection and disease when used in real-world conditions. They can also address important questions that were either not studied or were incompletely studied in the trials and that will inform evolving vaccine policy, including assessment of the duration of effectiveness; effectiveness in key subpopulations, such as the very old or immunocompromised; against severe disease and death due to COVID-19; against emerging SARS-CoV-2 variants of concern; and with different vaccination schedules, such as number of doses and varying dosing intervals. WHO convened an expert panel to develop interim best practice guidance for COVID-19 vaccine effectiveness evaluations. We present a summary of the interim guidance, including discussion of different study designs, priority outcomes to evaluate, potential biases, existing surveillance platforms that can be used, and recommendations for reporting results. |
U.S. Population-Based background incidence rates of medical conditions for use in safety assessment of COVID-19 vaccines.
Gubernot D , Jazwa A , Niu M , Baumblatt J , Gee J , Moro P , Duffy J , Harrington T , McNeil MM , Broder K , Su J , Kamidani S , Olson CK , Panagiotakopoulos L , Shimabukuro T , Forshee R , Anderson S , Bennett S . Vaccine 2021 39 (28) 3666-3677 The Coronavirus Disease 2019 (COVID-19) pandemic has had a devastating impact on global health, and has resulted in an unprecedented, international collaborative effort to develop vaccines to control the outbreak, protect human lives, and avoid further social and economic disruption. Mass vaccination campaigns are underway in multiple countries and are expected worldwide once more vaccine becomes available. Some early candidate vaccines use novel platforms, such as mRNA encapsulated in lipid nanoparticles, and relatively new platforms, such as replication-deficient viral vectors. While these new vaccine platforms hold promise, limited safety data in humans are available. Serious health outcomes linked to vaccinations are rare, and some outcomes may occur incidentally in the vaccinated population. Knowledge of background incidence rates of these medical conditions is a critical component of vaccine safety monitoring to aid in the assessment of adverse events temporally associated with vaccination and to put these events into context with what would be expected due to chance alone. A list of 22 potential adverse events of special interest (AESI), including neurologic, autoimmune, and cardiovascular disorders, was compiled by subject matter experts at the U.S. Food and Drug Administration and the Centers for Disease Control and Prevention. The most recently available U.S. background rates for these medical conditions, overall and by age, sex, and race/ethnicity (when available), were sourced from reported statistics (data published by medical panels/ associations or federal government reports), and literature reviews in PubMed. This review provides estimates of background incidence rates for medical conditions that may be monitored or studied as AESI during safety surveillance and research for COVID-19 vaccines and other new vaccines. |
Cumulative Risks from Stressor Exposures and Personal Risk Factors in the Workplace: Examples from a Scoping Review.
Fox MA , Niemeier RT , Hudson N , Siegel MR , Dotson GS . Int J Environ Res Public Health 2021 18 (11) Protecting worker and public health involves an understanding of multiple determinants, including exposures to biological, chemical, or physical agents or stressors in combination with other determinants including type of employment, health status, and individual behaviors. This has been illustrated during the COVID-19 pandemic by increased exposure and health risks for essential workers and those with pre-existing conditions, and mask-wearing behavior. Health risk assessment practices for environmental and occupational health typically do not incorporate multiple stressors in combination with personal risk factors. While conceptual developments in cumulative risk assessment to inform a more holistic approach to these real-life conditions have progressed, gaps remain, and practical methods and applications are rare. This scoping review characterizes existing evidence of combined stressor exposures and personal factors and risk to foster methods for occupational cumulative risk assessment. The review found examples from many workplaces, such as manufacturing, offices, and health care; exposures to chemical, physical, and psychosocial stressors combined with modifiable and unmodifiable determinants of health; and outcomes including respiratory function and disease, cancers, cardio-metabolic diseases, and hearing loss, as well as increased fertility, menstrual dysfunction and worsened mental health. To protect workers, workplace exposures and modifiable and unmodifiable characteristics should be considered in risk assessment and management. Data on combination exposures can improve assessments and risk estimates and inform protective exposure limits and management strategies. |
Coronavirus disease 2019 (COVID-19) Versus Influenza in Hospitalized Adult Patients in the United States: Differences in Demographic and Severity Indicators.
Talbot HK , Martin ET , Gaglani M , Middleton DB , Ghamande S , Silveira FP , Murthy K , Zimmerman RK , Trabue CH , Olson SM , Petrie JG , Ferdinands JM , Patel MM , Monto AS . Clin Infect Dis 2021 73 (12) 2240-2247 BACKGROUND: Novel coronavirus disease 2019 (COVID-19) is frequently compared with influenza. The Hospitalized Adult Influenza Vaccine Effectiveness Network (HAIVEN) conducts studies on the etiology and characteristics of U.S. hospitalized adults with influenza. It began enrolling patients with COVID-19 hospitalizations in March 2020. Patients with influenza were compared with those with COVID-19 in the first months of the U.S. epidemic. METHODS: Adults aged ≥ 18 years admitted to hospitals in 4 sites with acute respiratory illness were tested by real-time reverse transcription polymerase chain reaction for influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing COVID-19. Demographic and illness characteristics were collected for influenza illnesses during 3 seasons 2016-2019. Similar data were collected on COVID-19 cases admitted before June 19, 2020. RESULTS: Age groups hospitalized with COVID-19 (n = 914) were similar to those admitted with influenza (n = 1937); 80% of patients with influenza and 75% of patients with COVID-19 were aged ≥50 years. Deaths from COVID-19 that occurred in younger patients were less often related to underlying conditions. White non-Hispanic persons were overrepresented in influenza (64%) compared with COVID-19 hospitalizations (37%). Greater severity and complications occurred with COVID-19 including more ICU admissions (AOR = 15.3 [95% CI: 11.6, 20.3]), ventilator use (AOR = 15.6 [95% CI: 10.7, 22.8]), 7 additional days of hospital stay in those discharged alive, and death during hospitalization (AOR = 19.8 [95% CI: 12.0, 32.7]). CONCLUSIONS: While COVID-19 can cause a respiratory illness like influenza, it is associated with significantly greater severity of illness, longer hospital stays, and higher in-hospital deaths. |
Impact of Policy and Funding Decisions on COVID-19 Surveillance Operations and Case Reports - South Sudan, April 2020-February 2021.
Shragai T , Summers A , Olushayo O , Rumunu J , Mize V , Laku R , Bunga S . MMWR Morb Mortal Wkly Rep 2021 70 (22) 811-817 Early models predicted substantial COVID-19-associated morbidity and mortality across Africa (1-3). However, as of March 2021, countries in Africa are among those with the lowest reported incidence of COVID-19 worldwide (4). Whether this reflects effective mitigation, outbreak response, or demographic characteristics, (5) or indicates limitations in disease surveillance capacity is unclear (6). As countries implemented changes in funding, national policies, and testing strategies in response to the COVID-19 pandemic, surveillance capacity might have been adversely affected. This study assessed whether changes in surveillance operations affected reporting in South Sudan; testing and case numbers reported during April 6, 2020-February 21, 2021, were analyzed relative to the timing of funding, policy, and strategy changes.* South Sudan, with a population of approximately 11 million, began COVID-19 surveillance in February 2020 and reported 6,931 cases through February 21, 2021. Surveillance data analyzed were from point of entry screening, testing of symptomatic persons who contacted an alert hotline, contact tracing, sentinel surveillance, and outbound travel screening. After travel restrictions were relaxed in early May 2020, international land and air travel resumed and mandatory requirements for negative pretravel test results were initiated. The percentage of all testing accounted for by travel screening increased >300%, from 21.1% to 91.0% during the analysis period, despite yielding the lowest percentage of positive tests among all sources. Although testing of symptomatic persons and contact tracing yielded the highest percentage of COVID-19 cases, the percentage of all testing from these sources decreased 88%, from 52.6% to 6.3% after support for these activities was reduced. Collectively, testing increased over the project period, but shifted toward sources least likely to yield positive results, possibly resulting in underreporting of cases. Policy, funding, and strategy decisions related to the COVID-19 pandemic response, such as those implemented in South Sudan, are important issues to consider when interpreting the epidemiology of COVID-19 outbreaks. |
Detection of CTX-M-27 β-lactamase genes on two distinct plasmid types in ST38 Escherichia coli from three US states.
Cameron A , Mangat R , Taffner S , Wang J , Dumyati G , Stanton RA , Daniels JB , Campbell D , Lutgring JD , Pecora ND . Antimicrob Agents Chemother 2021 65 (7) e0082521 Infections caused by extended-spectrum β-lactamase (ESBL)-producing Escherichia coli are a significant cause of morbidity and healthcare costs. Globally, the prevailing clonal type is ST131 in association with the blaCTX-M-15 β-lactamase gene. However, other ESBLs such as blaCTX-M-14 and blaCTX-M-27 can also be prevalent in some regions. We identified ST38 ESBL-producing E. coli from different regions in the US which carry blaCTX-M-27 embedded on two distinct plasmid types, suggesting the potential emergence of new ESBL lineages. |
Effectiveness of Pfizer-BioNTech and Moderna Vaccines Against COVID-19 Among Hospitalized Adults Aged ≥65 Years - United States, January-March 2021.
Tenforde MW , Olson SM , Self WH , Talbot HK , Lindsell CJ , Steingrub JS , Shapiro NI , Ginde AA , Douin DJ , Prekker ME , Brown SM , Peltan ID , Gong MN , Mohamed A , Khan A , Exline MC , Files DC , Gibbs KW , Stubblefield WB , Casey JD , Rice TW , Grijalva CG , Hager DN , Shehu A , Qadir N , Chang SY , Wilson JG , Gaglani M , Murthy K , Calhoun N , Monto AS , Martin ET , Malani A , Zimmerman RK , Silveira FP , Middleton DB , Zhu Y , Wyatt D , Stephenson M , Baughman A , Womack KN , Hart KW , Kobayashi M , Verani JR , Patel MM . MMWR Morb Mortal Wkly Rep 2021 70 (18) 674-679 Adults aged ≥65 years are at increased risk for severe outcomes from COVID-19 and were identified as a priority group to receive the first COVID-19 vaccines approved for use under an Emergency Use Authorization (EUA) in the United States (1-3). In an evaluation at 24 hospitals in 14 states,* the effectiveness of partial or full vaccination(†) with Pfizer-BioNTech or Moderna vaccines against COVID-19-associated hospitalization was assessed among adults aged ≥65 years. Among 417 hospitalized adults aged ≥65 years (including 187 case-patients and 230 controls), the median age was 73 years, 48% were female, 73% were non-Hispanic White, 17% were non-Hispanic Black, 6% were Hispanic, and 4% lived in a long-term care facility. Adjusted vaccine effectiveness (VE) against COVID-19-associated hospitalization among adults aged ≥65 years was estimated to be 94% (95% confidence interval [CI] = 49%-99%) for full vaccination and 64% (95% CI = 28%-82%) for partial vaccination. These findings are consistent with efficacy determined from clinical trials in the subgroup of adults aged ≥65 years (4,5). This multisite U.S. evaluation under real-world conditions suggests that vaccination provided protection against COVID-19-associated hospitalization among adults aged ≥65 years. Vaccination is a critical tool for reducing severe COVID-19 in groups at high risk. |
World Malaria Day 2021: Commemorating 15 Years of Contribution by the United States President's Malaria Initiative.
Steketee RW , Choi M , Linn A , Florey L , Murphy M , Panjabi R . Am J Trop Med Hyg 2021 104 (6) 1955-1959 World Malaria Day 2021 coincides with the 15th anniversary of the United States President's Malaria Initiative (PMI) and follows the first anniversary of the declaration of the coronavirus disease (COVID-19) pandemic. From 2006 to the present, the PMI has led to considerable country-managed progress in malaria prevention, care, and treatment in 24 of the highest-burden countries in sub-Saharan Africa and three countries in the Southeast Asia Greater Mekong subregion. Furthermore, it has contributed to a 29% reduction in malaria cases and a 60% reduction in the death rates in sub-Saharan Africa. In this context of progress, substantial heterogeneity persists within and between countries, such that malaria control programs can seek subnational elimination in some populations but others still experience substantial malaria disease and death. During the COVID-19 pandemic, most malaria programs have shown resilience in delivering prevention campaigns, but many experienced important disruptions in their care and treatment of malaria illness. Confronting the COVID-19 pandemic and building on the progress against malaria will require fortitude, including strengthening the quality and ensuring the safety and resiliency of the existing programs, extending services to those currently not reached, and supporting the people and partners closest to those in need. |
Postvaccination SARS-CoV-2 Infections Among Skilled Nursing Facility Residents and Staff Members - Chicago, Illinois, December 2020-March 2021.
Teran RA , Walblay KA , Shane EL , Xydis S , Gretsch S , Gagner A , Samala U , Choi H , Zelinski C , Black SR . MMWR Morb Mortal Wkly Rep 2021 70 (17) 632-638 Early studies suggest that COVID-19 vaccines protect against severe illness (1); however, postvaccination SARS-CoV-2 infections (i.e., breakthrough infections) can occur because COVID-19 vaccines do not offer 100% protection (2,3). Data evaluating the occurrence of breakthrough infections and impact of vaccination in decreasing transmission in congregate settings are limited. Skilled nursing facility (SNF) residents and staff members have been disproportionately affected by SARS-CoV-2, the virus that causes COVID-19 (4,5), and were prioritized for COVID-19 vaccination (6,7). Starting December 28, 2020, all 78 Chicago-based SNFs began COVID-19 vaccination clinics over several weeks through the federal Pharmacy Partnership for Long-Term Care Program (PPP).(†) In February 2021, through routine screening, the Chicago Department of Public Health (CDPH) identified a SARS-CoV-2 infection in a SNF resident >14 days after receipt of the second dose of a two-dose COVID-19 vaccination series. SARS-CoV-2 cases, vaccination status, and possible vaccine breakthrough infections were identified by matching facility reports with state case and vaccination registries. Among 627 persons with SARS-CoV-2 infection across 75 SNFs since vaccination clinics began, 22 SARS-CoV-2 infections were identified among 12 residents and 10 staff members across 15 facilities ≥14 days after receiving their second vaccine dose (i.e., breakthrough infections in fully vaccinated persons). Nearly two thirds (14 of 22; 64%) of persons with breakthrough infections were asymptomatic; two residents were hospitalized because of COVID-19, and one died. No facility-associated secondary transmission occurred. Although few SARS-CoV-2 infections in fully vaccinated persons were observed, these cases demonstrate the need for SNFs to follow recommended routine infection prevention and control practices and promote high vaccination coverage among SNF residents and staff members. |
A community-driven resource for genomic epidemiology and antimicrobial resistance prediction of Neisseria gonorrhoeae at Pathogenwatch.
Sánchez-Busó L , Yeats CA , Taylor B , Goater RJ , Underwood A , Abudahab K , Argimón S , Ma KC , Mortimer TD , Golparian D , Cole MJ , Grad YH , Martin I , Raphael BH , Shafer WM , Town K , Wi T , Harris SR , Unemo M , Aanensen DM . Genome Med 2021 13 (1) 61 BACKGROUND: Antimicrobial-resistant (AMR) Neisseria gonorrhoeae is an urgent threat to public health, as strains resistant to at least one of the two last-line antibiotics used in empiric therapy of gonorrhoea, ceftriaxone and azithromycin, have spread internationally. Whole genome sequencing (WGS) data can be used to identify new AMR clones and transmission networks and inform the development of point-of-care tests for antimicrobial susceptibility, novel antimicrobials and vaccines. Community-driven tools that provide an easy access to and analysis of genomic and epidemiological data is the way forward for public health surveillance. METHODS: Here we present a public health-focussed scheme for genomic epidemiology of N. gonorrhoeae at Pathogenwatch ( https://pathogen.watch/ngonorrhoeae ). An international advisory group of experts in epidemiology, public health, genetics and genomics of N. gonorrhoeae was convened to inform on the utility of current and future analytics in the platform. We implement backwards compatibility with MLST, NG-MAST and NG-STAR typing schemes as well as an exhaustive library of genetic AMR determinants linked to a genotypic prediction of resistance to eight antibiotics. A collection of over 12,000 N. gonorrhoeae genome sequences from public archives has been quality-checked, assembled and made public together with available metadata for contextualization. RESULTS: AMR prediction from genome data revealed specificity values over 99% for azithromycin, ciprofloxacin and ceftriaxone and sensitivity values around 99% for benzylpenicillin and tetracycline. A case study using the Pathogenwatch collection of N. gonorrhoeae public genomes showed the global expansion of an azithromycin-resistant lineage carrying a mosaic mtr over at least the last 10 years, emphasising the power of Pathogenwatch to explore and evaluate genomic epidemiology questions of public health concern. CONCLUSIONS: The N. gonorrhoeae scheme in Pathogenwatch provides customised bioinformatic pipelines guided by expert opinion that can be adapted to public health agencies and departments with little expertise in bioinformatics and lower-resourced settings with internet connection but limited computational infrastructure. The advisory group will assess and identify ongoing public health needs in the field of gonorrhoea, particularly regarding gonococcal AMR, in order to further enhance utility with modified or new analytic methods. |
The first sixty days of COVID-19 in a humanitarian response setting: a descriptive epidemiological analysis of the outbreak in South Sudan.
Waya JLL , Lako R , Bunga S , Chun H , Mize V , Ambani B , Wamala JF , Guyo AG , Gray JH , Gai M , Maleghemi S , Kol M , Rumunu J , Tukuru M , Olu OO . Pan Afr Med J 2020 37 384 INTRODUCTION: the coronavirus disease 2019 (COVID-19) was declared a pandemic on March 11, 2020. South Sudan, a low-income and humanitarian response setting, reported its first case of COVID-19 on April 5, 2020. We describe the socio-demographic and epidemiologic characteristics of COVID-19 cases in this setting. METHODS: we conducted a cross-sectional descriptive analysis of data for 1,330 confirmed COVID-19 cases from the first 60 days of the outbreak. RESULTS: among the 1,330 confirmed cases, the mean age was 37.1 years, 77% were male, 17% were symptomatic with 95% categorized as mild, and the case fatality rate was 1.1%. Only 24.7% of cases were detected through alerts and sentinel site surveillance, with 95% of the cases reported from the capital, Juba. Epidemic doubling time averaged 9.8 days (95% confidence interval [CI] 7.7 - 13.4), with an attack rate of 11.5 per 100,000 population. Test positivity rate was 18.2%, with test rate per 100,000 population of 53 and mean test turn-around time of 9 days. The case to contact ratio was 1: 2.2. CONCLUSION: this 2-month initial period of COVID-19 in South Sudan demonstrated mostly young adults and men affected, with most cases reported as asymptomatic. Systems´ limitations highlighted included a small proportion of cases detected through surveillance, low testing rates, low contact elicitation, and long collection to test turn-around times limiting the country´s ability to effectively respond to the outbreak. A multi-pronged response including greater access to testing, scale-up of surveillance, contact tracing and community engagement, among other interventions are needed to improve the COVID-19 response in this setting. |
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