Last data update: May 06, 2024. (Total: 46732 publications since 2009)
Records 1-6 (of 6 Records) |
Query Trace: Messina L[original query] |
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A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk
Lim AY , Jafari Y , Caldwell JM , Clapham HE , Gaythorpe KAM , Hussain-Alkhateeb L , Johansson MA , Kraemer MUG , Maude RJ , McCormack CP , Messina JP , Mordecai EA , Rabe IB , Reiner RC Jr , Ryan SJ , Salje H , Semenza JC , Rojas DP , Brady OJ . BMC Infect Dis 2023 23 (1) 708 BACKGROUND: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. METHODS: We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). RESULTS: We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. CONCLUSIONS: Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping. |
A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk (preprint)
Lim AY , Jafari Y , Caldwell JM , Clapham HE , Gaythorpe KAM , Hussain-Alkhateeb L , Johansson MA , Kraemer MUG , Maude RJ , McCormack CP , Messina JP , Mordecai EA , Rabe IB , Reiner RC , Ryan SJ , Salje H , Semenza JC , Rojas DP , Brady OJ . medRxiv 2023 20 Background Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedesborne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. Methods We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc). Results We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 183 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, ii) regional models used to predict the spread of major epidemics between countries and iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 33/148) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc) and only 48% of studies assessed predictive performance via out-of-sample validation procedures. Conclusions Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We outline specific recommendations for future studies regarding aims and data choice, covariate selection, model formulation and evaluation. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license. |
Coronavirus Disease 2019 (COVID-19) in a Patient with Disseminated Histoplasmosis and HIV-A Case Report from Argentina and Literature Review.
Messina FA , Marin E , Caceres DH , Romero M , Depardo R , Priarone MM , Rey L , Vázquez M , Verweij PE , Chiller TM , Santiso G . J Fungi (Basel) 2020 6 (4) The disease caused by the new SARS-CoV-2, known as Coronavirus disease 2019 (COVID-19), was first identified in China in December 2019 and rapidly spread around the world. Coinfections with fungal pathogens in patients with COVID-19 add challenges to patient care. We conducted a literature review on fungal coinfections in patients with COVID-19. We describe a report of a patient with disseminated histoplasmosis who was likely infected with SARS-CoV-2 and experienced COVID-19 during hospital care in Buenos Aires, Argentina. This patient presented with advanced HIV disease, a well-known factor for disseminated histoplasmosis; on the other hand, we suspected that COVID-19 was acquired during hospitalization but there is not enough evidence to support this hypothesis. Clinical correlation and the use of specific Histoplasma and COVID-19 rapid diagnostics assays were key to the timely diagnosis of both infections, permitting appropriate treatment and patient care. |
Methods to include persons living with HIV not receiving HIV care in the Medical Monitoring Project
Wei SC , Messina L , Hood J , Hughes A , Jaenicke T , Johnson K , Mena L , Scheer S , Udeagu CC , Wohl A , Robertson M , Prejean J , Chen M , Tang T , Bertolli J , Johnson CH , Skarbinski J . PLoS One 2019 14 (8) e0219996 The Medical Monitoring Project (MMP) is an HIV surveillance system that provides national estimates of HIV-related behaviors and clinical outcomes. When first implemented, MMP excluded persons living with HIV not receiving HIV care. This analysis will describe new case-surveillance-based methods to identify and recruit persons living with HIV who are out of care and at elevated risk for mortality and ongoing HIV transmission. Stratified random samples of all persons living with HIV were selected from the National HIV Surveillance System in five public health jurisdictions from 2012-2014. Sampled persons were located and contacted through seven different data sources and five methods of contact to collect interviews and medical record abstractions. Data were weighted for non-response and case reporting delay. The modified sampling methodology yielded 1159 interviews (adjusted response rate, 44.5%) and matching medical record abstractions for 1087 (93.8%). Of persons with both interview and medical record data, 264 (24.3%) would not have been included using prior MMP methods. Significant predictors were identified for successful contact (e.g., retention in care, adjusted Odds Ratio [aOR] 5.02; 95% Confidence Interval [CI] 1.98-12.73), interview (e.g. moving out of jurisdiction, aOR 0.24; 95% CI: 0.12-0.46) and case reporting delay (e.g. rural residence, aOR 3.18; 95% CI: 2.09-4.85). Case-surveillance-based sampling resulted in a comparable response rate to existing MMP methods while providing information on an important new population. These methods have since been adopted by the nationally representative MMP surveillance system, offering a model for public health program, research and surveillance endeavors seeking inclusion of all persons living with HIV. |
Explaining variation in adult Anopheles indoor resting abundance: the relative effects of larval habitat proximity and insecticide-treated bed net use
McCann RS , Messina JP , MacFarlane DW , Bayoh MN , Gimnig JE , Giorgi E , Walker ED . Malar J 2017 16 (1) 288 BACKGROUND: Spatial determinants of malaria risk within communities are associated with heterogeneity of exposure to vector mosquitoes. The abundance of adult malaria vectors inside people's houses, where most transmission takes place, should be associated with several factors: proximity of houses to larval habitats, structural characteristics of houses, indoor use of vector control tools containing insecticides, and human behavioural and environmental factors in and near houses. While most previous studies have assessed the association of larval habitat proximity in landscapes with relatively low densities of larval habitats, in this study these relationships were analysed in a region of rural, lowland western Kenya with high larval habitat density. METHODS: 525 houses were sampled for indoor-resting mosquitoes across an 8 by 8 km study area using the pyrethrum spray catch method. A predictive model of larval habitat location in this landscape, previously verified, provided derivations of indices of larval habitat proximity to houses. Using geostatistical regression models, the association of larval habitat proximity, long-lasting insecticidal nets (LLIN) use, house structural characteristics (wall type, roof type), and peridomestic variables (cooking in the house, cattle near the house, number of people sleeping in the house) with mosquito abundance in houses was quantified. RESULTS: Vector abundance was low (mean, 1.1 adult Anopheles per house). Proximity of larval habitats was a strong predictor of Anopheles abundance. Houses without an LLIN had more female Anopheles gambiae s.s., Anopheles arabiensis and Anopheles funestus than houses where some people used an LLIN (rate ratios, 95% CI 0.87, 0.85-0.89; 0.84, 0.82-0.86; 0.38, 0.37-0.40) and houses where everyone used an LLIN (RR, 95% CI 0.49, 0.48-0.50; 0.39, 0.39-0.40; 0.60, 0.58-0.61). Cooking in the house also reduced Anopheles abundance across all species. The number of people sleeping in the house, presence of cattle near the house, and house structure modulated Anopheles abundance, but the effect varied with Anopheles species and sex. CONCLUSIONS: Variation in the abundance of indoor-resting Anopheles in rural houses of western Kenya varies with clearly identifiable factors. Results suggest that LLIN use continues to function in reducing vector abundance, and that larval source management in this region could lead to further reductions in malaria risk by reducing the amount of an obligatory resource for mosquitoes near people's homes. |
Modeling larval malaria vector habitat locations using landscape features and cumulative precipitation measures
McCann RS , Messina JP , MacFarlane DW , Bayoh MN , Vulule JM , Gimnig JE , Walker ED . Int J Health Geogr 2014 13 17 BACKGROUND: Predictive models of malaria vector larval habitat locations may provide a basis for understanding the spatial determinants of malaria transmission. METHODS: We used four landscape variables (topographic wetness index [TWI], soil type, land use-land cover, and distance to stream) and accumulated precipitation to model larval habitat locations in a region of western Kenya through two methods: logistic regression and random forest. Additionally, we used two separate data sets to account for variation in habitat locations across space and over time. RESULTS: Larval habitats were more likely to be present in locations with a lower slope to contributing area ratio (i.e. TWI), closer to streams, with agricultural land use relative to nonagricultural land use, and in friable clay/sandy clay loam soil and firm, silty clay/clay soil relative to friable clay soil. The probability of larval habitat presence increased with increasing accumulated precipitation. The random forest models were more accurate than the logistic regression models, especially when accumulated precipitation was included to account for seasonal differences in precipitation. The most accurate models for the two data sets had area under the curve (AUC) values of 0.864 and 0.871, respectively. TWI, distance to the nearest stream, and precipitation had the greatest mean decrease in Gini impurity criteria in these models. CONCLUSIONS: This study demonstrates the usefulness of random forest models for larval malaria vector habitat modeling. TWI and distance to the nearest stream were the two most important landscape variables in these models. Including accumulated precipitation in our models improved the accuracy of larval habitat location predictions by accounting for seasonal variation in the precipitation. Finally, the sampling strategy employed here for model parameterization could serve as a framework for creating predictive larval habitat models to assist in larval control efforts. |
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