Last data update: May 13, 2024. (Total: 46773 publications since 2009)
Records 1-7 (of 7 Records) |
Query Trace: Clapham H[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. |
Knowledge gaps in the epidemiology of severe dengue impede vaccine evaluation
Sharp TM , Anderson KB , Katzelnick LC , Clapham H , Johansson MA , Morrison AC , Harris E , Paz-Bailey G , Waterman SH . Lancet Infect Dis 2021 22 (2) e42-e51 The most severe consequences of dengue virus infection include shock, haemorrhage, and major organ failure; however, the frequency of these manifestations varies, and the relative contribution of pre-existing anti-dengue virus antibodies, virus characteristics, and host factors (including age and comorbidities) are not well understood. Reliable characterisation of the epidemiology of severe dengue first depends on the use of consistent definitions of disease severity. As vaccine trials have shown, severe dengue is a crucial interventional endpoint, yet the infrequency of its occurrence necessitates the inclusion of thousands of study participants to appropriately compare its frequency among participants who have and have not been vaccinated. Hospital admission is frequently used as a proxy for severe dengue; however, lack of specificity and variability in clinical practices limit the reliability of this approach. Although previous infection with a dengue virus is the best characterised risk factor for developing severe dengue, the influence of the timing between dengue virus infections and the sequence of dengue virus infections on disease severity is only beginning to be elucidated. To improve our understanding of the diverse factors that shape the clinical spectrum of disease resulting from dengue virus infection, prospective, community-based and clinic-based immunological, virological, genetic, and clinical studies across a range of ages and geographical regions are needed. |
Mortality surveillance during the COVID-19 pandemic.
Setel P , AbouZahr C , Atuheire EB , Bratschi M , Cercone E , Chinganya O , Clapham B , Clark SJ , Congdon C , de Savigny D , Karpati A , Nichols E , Jakob R , Mwanza J , Muhwava W , Nahmias P , Ortiz EM , Tshangela A . Bull World Health Organ 2020 98 (6) 374 During an epidemic, rapid mortality surveillance provides essential information to formulate an evidence-based response. Weekly counts of deaths are a key indicator of overall epidemic impact and trajectory.1,2 Enumeration of all deaths, when compared to historically expected mortality, produces a picture of excess death, capturing both the direct burden of the epidemic and its indirect mortality burden, caused by disruptions to the access, use and provision of health-care services. Such actionable public health intelligence can overcome the ambiguities of just measuring cases and deaths linked to the infectious disease causing the epidemic. Measuring excess death would therefore be useful in the countries’ response to the coronavirus disease 2019 (COVID-19) pandemic. |
An open challenge to advance probabilistic forecasting for dengue epidemics.
Johansson MA , Apfeldorf KM , Dobson S , Devita J , Buczak AL , Baugher B , Moniz LJ , Bagley T , Babin SM , Guven E , Yamana TK , Shaman J , Moschou T , Lothian N , Lane A , Osborne G , Jiang G , Brooks LC , Farrow DC , Hyun S , Tibshirani RJ , Rosenfeld R , Lessler J , Reich NG , Cummings DAT , Lauer SA , Moore SM , Clapham HE , Lowe R , Bailey TC , Garcia-Diez M , Carvalho MS , Rodo X , Sardar T , Paul R , Ray EL , Sakrejda K , Brown AC , Meng X , Osoba O , Vardavas R , Manheim D , Moore M , Rao DM , Porco TC , Ackley S , Liu F , Worden L , Convertino M , Liu Y , Reddy A , Ortiz E , Rivero J , Brito H , Juarrero A , Johnson LR , Gramacy RB , Cohen JM , Mordecai EA , Murdock CC , Rohr JR , Ryan SJ , Stewart-Ibarra AM , Weikel DP , Jutla A , Khan R , Poultney M , Colwell RR , Rivera-Garcia B , Barker CM , Bell JE , Biggerstaff M , Swerdlow D , Mier YTeran-Romero L , Forshey BM , Trtanj J , Asher J , Clay M , Margolis HS , Hebbeler AM , George D , Chretien JP . Proc Natl Acad Sci U S A 2019 116 (48) 24268-24274 A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue. |
Immune status alters the probability of apparent illness due to dengue virus infection: Evidence from a pooled analysis across multiple cohort and cluster studies
Clapham HE , Cummings DAT , Johansson MA . PLoS Negl Trop Dis 2017 11 (9) e0005926 Dengue is an important vector-borne pathogen found across much of the world. Many factors complicate our understanding of the relationship between infection with one of the four dengue virus serotypes, and the observed incidence of disease. One of the factors is a large proportion of infections appear to result in no or few symptoms, while others result in severe infections. Estimates of the proportion of infections that result in no symptoms (inapparent) vary widely from 8% to 100%, depending on study and setting. To investigate the sources of variation of these estimates, we used a flexible framework to combine data from multiple cohort studies and cluster studies (follow-up around index cases). Building on previous observations that the immune status of individuals affects their probability of apparent disease, we estimated the probability of apparent disease among individuals with different exposure histories. In cohort studies mostly assessing infection in children, we estimated the proportion of infections that are apparent as 0.18 (95% Credible Interval, CI: 0.16, 0.20) for primary infections, 0.13 (95% CI: 0.05, 0.17) for individuals infected in the year following a first infection (cross-immune period), and 0.41 (95% CI: 0.36, 0.45) for those experiencing secondary infections after this first year. Estimates of the proportion of infections that are apparent from cluster studies were slightly higher than those from cohort studies for both primary and secondary infections, 0.22 (95% CI: 0.15, 0.29) and 0.57 (95% CI: 0.49, 0.68) respectively. We attempted to estimate the apparent proportion by serotype, but current published data were too limited to distinguish the presence or absence of serotype-specific differences. These estimates are critical for understanding dengue epidemiology. Most dengue data come from passive surveillance systems which not only miss most infections because they are asymptomatic and often underreported, but will also vary in sensitivity over time due to the interaction between previous incidence and the symptomatic proportion, as shown here. Nonetheless the underlying incidence of infection is critical to understanding susceptibility of the population and estimating the true burden of disease, key factors for effectively targeting interventions. The estimates shown here help clarify the link between past infection, observed disease, and current transmission intensity. |
Enhancing disease surveillance with novel data streams: challenges and opportunities
Althouse BM , Scarpino SV , Meyers LA , Ayers JW , Bargsten M , Baumbach J , Brownstein JS , Castro L , Clapham H , Cummings DAT , Del Valle S , Eubank S , Fairchild G , Finelli L , Generous N , George D , Harper DR , Hébert-Dufresne L , Johansson MA , Konty K , Lipsitch M , Milinovich G , Miller JD , Nsoesie EO , Olson DR , Paul M , Polgreen PM , Priedhorsky R , Read JM , Rodríguez-Barraquer I , Smith DJ , Stefansen C , Swerdlow DL , Thompson D , Vespignani A , Wesolowski A . EPJ Data Sci 2015 4 (1) 17 Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature. |
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