Publications by authors named "Felipe J Colón-González"

20 Publications

  • Page 1 of 1

Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study.

Lancet Planet Health 2021 Apr;5(4):e209-e219

Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK; Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, UK; Centre for Statistical Modelling, London School of Hygiene & Tropical Medicine, London, UK.

Background: Temperature and rainfall patterns are known to influence seasonal patterns of dengue transmission. However, the effect of severe drought and extremely wet conditions on the timing and intensity of dengue epidemics is poorly understood. In this study, we aimed to quantify the non-linear and delayed effects of extreme hydrometeorological hazards on dengue risk by level of urbanisation in Brazil using a spatiotemporal model.

Methods: We combined distributed lag non-linear models with a spatiotemporal Bayesian hierarchical model framework to determine the exposure-lag-response association between the relative risk (RR) of dengue and a drought severity index. We fit the model to monthly dengue case data for the 558 microregions of Brazil between January, 2001, and January, 2019, accounting for unobserved confounding factors, spatial autocorrelation, seasonality, and interannual variability. We assessed the variation in RR by level of urbanisation through an interaction between the drought severity index and urbanisation. We also assessed the effect of hydrometeorological hazards on dengue risk in areas with a high frequency of water supply shortages.

Findings: The dataset included 12 895 293 dengue cases reported between 2001 and 2019 in Brazil. Overall, the risk of dengue increased between 0-3 months after extremely wet conditions (maximum RR at 1 month lag 1·56 [95% CI 1·41-1·73]) and 3-5 months after drought conditions (maximum RR at 4 months lag 1·43 [1·22-1·67]). Including a linear interaction between the drought severity index and level of urbanisation improved the model fit and showed the risk of dengue was higher in more rural areas than highly urbanised areas during extremely wet conditions (maximum RR 1·77 [1·32-2·37] at 0 months lag vs maximum RR 1·58 [1·39-1·81] at 2 months lag), but higher in highly urbanised areas than rural areas after extreme drought (maximum RR 1·60 [1·33-1·92] vs 1·15 [1·08-1·22], both at 4 months lag). We also found the dengue risk following extreme drought was higher in areas that had a higher frequency of water supply shortages.

Interpretation: Wet conditions and extreme drought can increase the risk of dengue with different delays. The risk associated with extremely wet conditions was higher in more rural areas and the risk associated with extreme drought was exacerbated in highly urbanised areas, which have water shortages and intermittent water supply during droughts. These findings have implications for targeting mosquito control activities in poorly serviced urban areas, not only during the wet and warm season, but also during drought periods.

Funding: Royal Society, Medical Research Council, Wellcome Trust, National Institutes of Health, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, and Conselho Nacional de Desenvolvimento Científico e Tecnológico.

Translation: For the Portuguese translation of the abstract see Supplementary Materials section.
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http://dx.doi.org/10.1016/S2542-5196(20)30292-8DOI Listing
April 2021

Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles.

PLoS Med 2021 Mar 4;18(3):e1003542. Epub 2021 Mar 4.

Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom.

Background: With enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare.

Methods And Findings: We introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002-2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6-148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5-80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102-575) than those made with the baseline model (CRPS = 125, 95% CI 120-168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts and serotype-specific case data.

Conclusions: This study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems.
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http://dx.doi.org/10.1371/journal.pmed.1003542DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7971894PMC
March 2021

Demographic and socioeconomic patterns in healthcare-seeking behaviour for respiratory symptoms in England: a comparison with non-respiratory symptoms and between three healthcare services.

BMJ Open 2020 11 6;10(11):e038356. Epub 2020 Nov 6.

School of Environmental Sciences, University of East Anglia, Norwich, UK.

Objective: This study will analyse respiratory contacts to three healthcare services that capture more of the community disease burden than acute data sources, such as hospitalisations. The objective is to explore associations between contacts to these services and the patient's age, gender and deprivation. Results will be compared between healthcare services, and with non-respiratory contacts to explore how contacts differ by service and illness. It is crucial to investigate the sociodemographic patterns in healthcare-seeking behaviour to enable targeted public health interventions.

Design: Ecological study.

Setting: Surveillance of respiratory contacts to three healthcare services in England: telehealth helpline (NHS111); general practitioner in-hours (GPIH); and general practitioner out of hours unscheduled care (GPOOH).

Participants: 13 million respiratory contacts to NHS111, GPIH and GPOOH.

Outcome Measures: Respiratory contacts to NHS111, GPIH and GPOOH, and non-respiratory contacts to NHS111 and GPOOH.

Results: More respiratory contacts were observed for females, with 1.59, 1.73, and 1.95 times the rate of contacts to NHS111, GPOOH and GPIH, respectively. When compared with 15-44 year olds, there were 37.32, 18.66 and 6.21 times the rate of respiratory contacts to NHS111, GPOOH and GPIH in children <1 year. There were 1.75 and 2.70 times the rate of respiratory contacts in the most deprived areas compared with the least deprived to NHS111 and GPOOH. Elevated respiratory contacts were observed for males <5 years compared with females <5 years. Healthcare-seeking behaviours between respiratory and non-respiratory contacts were similar.

Conclusion: When contacts to services that capture more of the disease burden are explored, the demographic patterns are similar to those described in the literature for acute systems. Comparable results were observed between respiratory and non-respiratory contacts suggesting that when a wider spectrum of disease is explored, sociodemographic factors may be the strongest influencers of healthcare-seeking behaviour.
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http://dx.doi.org/10.1136/bmjopen-2020-038356DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7651740PMC
November 2020

Spatiotemporal and Socioeconomic Risk Factors for Dengue at the Province Level in Vietnam, 2013-2015: Clustering Analysis and Regression Model.

Trop Med Infect Dis 2020 May 19;5(2). Epub 2020 May 19.

Department of Global Health and Development, London School of Hygiene & Tropical Medicine, London WC1H 9SH, UK.

Dengue is a serious infectious disease threat in Vietnam, but its spatiotemporal and socioeconomic risk factors are not currently well understood at the province level across the country and on a multiannual scale. We explore spatial trends, clusters and outliers in dengue case counts at the province level from 2011-2015 and use this to extract spatiotemporal variables for regression analysis of the association between dengue case counts and selected spatiotemporal and socioeconomic variables from 2013-2015. Dengue in Vietnam follows anticipated spatial trends, with a potential two-year cycle of high-high clusters in some southern provinces. Small but significant associations are observed between dengue case counts and mobility, population density, a province's dengue rates the previous year, and average dengue rates two years previous in first and second order contiguous neighbours. Significant associations were not found between dengue case counts and housing pressure, access to electricity, clinician density, province-adjusted poverty rate, percentage of children below one vaccinated, or percentage of population in urban settings. These findings challenge assumptions about socioeconomic and spatiotemporal risk factors for dengue, and support national prevention targeting in Vietnam at the province level. They may also be of wider relevance for the study of other arboviruses, including Japanese encephalitis, Zika, and Chikungunya.
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http://dx.doi.org/10.3390/tropicalmed5020081DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7345007PMC
May 2020

Dynamical Malaria Forecasts Are Skillful at Regional and Local Scales in Uganda up to 4 Months Ahead.

Geohealth 2019 Mar 22;3(3):58-66. Epub 2019 Mar 22.

Ministry of Health Kampala Uganda.

Malaria forecasts from dynamical systems have never been attempted at the health district or local clinic catchment scale, and so their usefulness for public health preparedness and response at the local level is fundamentally unknown. A pilot preoperational forecasting system is introduced in which the European Centre for Medium Range Weather Forecasts ensemble prediction system and seasonal climate forecasts of temperature and rainfall are used to drive the uncalibrated dynamical malaria model VECTRI to predict anomalies in transmission intensity 4 months ahead. It is demonstrated that the system has statistically significant skill at a number of sentinel sites in Uganda with high-quality data. Skill is also found at approximately 50% of the Ugandan health districts despite inherent uncertainties of unconfirmed health reports. A cost-loss economic analysis at three example sentinel sites indicates that the forecast system can have a positive economic benefit across a broad range of intermediate cost-loss ratios and frequency of transmission anomalies. We argue that such an analysis is a necessary first step in the attempt to translate climate-driven malaria information to policy-relevant decisions.
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http://dx.doi.org/10.1029/2018GH000157DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038892PMC
March 2019

Can economic indicators predict infectious disease spread? A cross-country panel analysis of 13 European countries.

Scand J Public Health 2020 Jun 10;48(4):351-361. Epub 2019 Jul 10.

European Centre for Disease Prevention and Control (ECDC), Sweden.

It is unclear how economic factors impact on the epidemiology of infectious disease. We evaluated the relationship between incidence of selected infectious diseases and economic factors, including economic downturn, in 13 European countries between 1970 and 2010. : Data were obtained from national communicable disease surveillance centres. Negative binomial forms of the generalised additive model (GAM) and the generalised linear model were tested to see which best reflected transmission dynamics of: diphtheria, pertussis, measles, meningococcal disease, hepatitis B, gonorrhoea, syphilis, hepatitis A and salmonella. Economic indicators were gross domestic product per capita (GDPpc), unemployment rates and (economic) downturn. : GAM models produced the best goodness-of-fit results. The relationship between GDPpc and disease incidence was often non-linear. Strength and directions of association between population age, tertiary education levels, GDPpc and unemployment were disease dependent. Overdispersion for almost all diseases validated the assumption of a negative binomial relationship. Downturns were not independently linked to disease incidence.
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http://dx.doi.org/10.1177/1403494819852830DOI Listing
June 2020

Ragweed pollen and allergic symptoms in children: Results from a three-year longitudinal study.

Sci Total Environ 2019 Sep 21;683:240-248. Epub 2019 May 21.

School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK. Electronic address:

Common ragweed is a highly allergenic invasive species in Europe, expected to become widespread under climate change. Allergy to ragweed manifests as eye, nasal and lung symptoms, and children may retain these throughout life. The dose-response relationship between symptoms and pollen concentrations is unclear. We undertook a longitudinal study, assessing the association between ragweed pollen concentration and allergic eye, nasal and lung symptoms in children living under a range of ragweed pollen concentrations in Croatia. Over three years, 85 children completed daily diaries, detailing allergic symptoms alongside daily location, activities and medication, resulting in 10,130 individual daily entries. The daily ragweed pollen concentration for the children's locations was obtained, alongside daily weather and air pollution. Parents completed a home/lifestyle/medical questionnaire. Generalised Additive Mixed Models established the relationship between pollen concentrations and symptoms, alongside other covariates. Eye symptoms were associated with mean daily pollen concentration over four days (day of symptoms plus 3 previous days); 61 grains/m/day (95%CI: 45, 100) was the threshold at which 50% of children reported symptoms. Nasal symptoms were associated with mean daily pollen concentration over 12 days (day of symptoms plus 11 previous days); the threshold for 50% of children reporting symptoms was 40 grains/m/day (95%CI: 24, 87). Lung symptoms showed a relationship with mean daily pollen concentration over 19 days (day of symptoms plus 18 previous days), with a threshold of 71 grains/m/day (95%CI: 59, 88). Taking medication on the day of symptoms showed higher odds, suggesting responsive behaviour. Taking medication on the day prior to symptoms showed lower odds of reporting, indicating preventative behaviour. Different symptoms in children demonstrate varying dose-response relationships with ragweed pollen concentrations. Each symptom type responded to pollen exposure over different time periods. Using medication prior to symptoms can reduce symptom presence. These findings can be used to better manage paediatric ragweed allergy symptoms.
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http://dx.doi.org/10.1016/j.scitotenv.2019.05.284DOI Listing
September 2019

Seasonality and the effects of weather on Campylobacter infections.

BMC Infect Dis 2019 Mar 13;19(1):255. Epub 2019 Mar 13.

Statistics, Modelling and Economics Department, National Infection Service, Public Health England, 61, Colindale Avenue, London, NW9 5EQ, UK.

Background: Campylobacteriosis is a major public health concern. The weather factors that influence spatial and seasonal distributions are not fully understood.

Methods: To investigate the impacts of temperature and rainfall on Campylobacter infections in England and Wales, cases of Campylobacter were linked to local temperature and rainfall at laboratory postcodes in the 30 days before the specimen date. Methods for investigation included a comparative conditional incidence, wavelet, clustering, and time series analyses.

Results: The increase of Campylobacter infections in the late spring was significantly linked to temperature two weeks before, with an increase in conditional incidence of 0.175 cases per 100,000 per week for weeks 17 to 24; the relationship to temperature was not linear. Generalized structural time series model revealed that changes in temperature accounted for 33.3% of the expected cases of Campylobacteriosis, with an indication of the direction and relevant temperature range. Wavelet analysis showed a strong annual cycle with additional harmonics at four and six months. Cluster analysis showed three clusters of seasonality with geographic similarities representing metropolitan, rural, and other areas.

Conclusions: The association of Campylobacteriosis with temperature is likely to be indirect. High-resolution spatial temporal linkage of weather parameters and cases is important in improving weather associations with infectious diseases. The primary driver of Campylobacter incidence remains to be determined; other avenues, such as insect contamination of chicken flocks through poor biosecurity should be explored.
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http://dx.doi.org/10.1186/s12879-019-3840-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417031PMC
March 2019

Comparison of statistical algorithms for daily syndromic surveillance aberration detection.

Bioinformatics 2019 09;35(17):3110-3118

Population Evidence and Technologies, Warwick Medical School, University of Warwick, Coventry, UK.

Motivation: Public health authorities can provide more effective and timely interventions to protect populations during health events if they have effective multi-purpose surveillance systems. These systems rely on aberration detection algorithms to identify potential threats within large datasets. Ensuring the algorithms are sensitive, specific and timely is crucial for protecting public health. Here, we evaluate the performance of three detection algorithms extensively used for syndromic surveillance: the 'rising activity, multilevel mixed effects, indicator emphasis' (RAMMIE) method and the improved quasi-Poisson regression-based method known as 'Farrington Flexible' both currently used at Public Health England, and the 'Early Aberration Reporting System' (EARS) method used at the US Centre for Disease Control and Prevention. We model the wide range of data structures encountered within the daily syndromic surveillance systems used by PHE. We undertake extensive simulations to identify which algorithms work best across different types of syndromes and different outbreak sizes. We evaluate RAMMIE for the first time since its introduction. Performance metrics were computed and compared in the presence of a range of simulated outbreak types that were added to baseline data.

Results: We conclude that amongst the algorithm variants that have a high specificity (i.e. >90%), Farrington Flexible has the highest sensitivity and specificity, whereas RAMMIE has the highest probability of outbreak detection and is the most timely, typically detecting outbreaks 2-3 days earlier.

Availability And Implementation: R codes developed for this project are available through https://github.com/FelipeJColon/AlgorithmComparison.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/bty997DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6736430PMC
September 2019

The influence of a major sporting event upon emergency department attendances; A retrospective cross-national European study.

PLoS One 2018 13;13(6):e0198665. Epub 2018 Jun 13.

NIHR HPRU in Gastrointestinal Infections, Liverpool, United Kingdom.

Major sporting events may influence attendance levels at hospital emergency departments (ED). Previous research has focussed on the impact of single games, or wins/losses for specific teams/countries, limiting wider generalisations. Here we explore the impact of the Euro 2016 football championships on ED attendances across four participating nations (England, France, Northern Ireland, Wales), using a single methodology. Match days were found to have no significant impact upon daily ED attendances levels. Focussing upon hourly attendances, ED attendances across all countries in the four hour pre-match period were statistically significantly lower than would be expected (OR 0.97, 95% CI 0.94-0.99) and further reduced during matches (OR 0.94, 95% CI 0.91-0.97). In the 4 hour post-match period there was no significant increase in attendances (OR 1.01, 95% CI 0.99-1.04). However, these impacts were highly variable between individual matches: for example in the 4 hour period following the final, involving France, the number of ED attendances in France increased significantly (OR 1.27, 95% CI 1.13-1.42). Overall our results indicate relatively small impacts of major sporting events upon ED attendances. The heterogeneity observed makes it difficult for health providers to predict how major sporting events may affect ED attendances but supports the future development of compatible systems in different countries to support cross-border public health surveillance.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0198665PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5999282PMC
January 2019

Limiting global-mean temperature increase to 1.5-2 °C could reduce the incidence and spatial spread of dengue fever in Latin America.

Proc Natl Acad Sci U S A 2018 06 29;115(24):6243-6248. Epub 2018 May 29.

School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, United Kingdom.

The Paris Climate Agreement aims to hold global-mean temperature well below 2 °C and to pursue efforts to limit it to 1.5 °C above preindustrial levels. While it is recognized that there are benefits for human health in limiting global warming to 1.5 °C, the magnitude with which those societal benefits will be accrued remains unquantified. Crucial to public health preparedness and response is the understanding and quantification of such impacts at different levels of warming. Using dengue in Latin America as a study case, a climate-driven dengue generalized additive mixed model was developed to predict global warming impacts using five different global circulation models, all scaled to represent multiple global-mean temperature assumptions. We show that policies to limit global warming to 2 °C could reduce dengue cases by about 2.8 (0.8-7.4) million cases per year by the end of the century compared with a no-policy scenario that warms by 3.7 °C. Limiting warming further to 1.5 °C produces an additional drop in cases of about 0.5 (0.2-1.1) million per year. Furthermore, we found that by limiting global warming we can limit the expansion of the disease toward areas where incidence is currently low. We anticipate our study to be a starting point for more comprehensive studies incorporating socioeconomic scenarios and how they may further impact dengue incidence. Our results demonstrate that although future climate change may amplify dengue transmission in the region, impacts may be avoided by constraining the level of warming.
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http://dx.doi.org/10.1073/pnas.1718945115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6004471PMC
June 2018

A methodological framework for the evaluation of syndromic surveillance systems: a case study of England.

BMC Public Health 2018 04 24;18(1):544. Epub 2018 Apr 24.

Real-time Syndromic Surveillance Team, National Infection Service, Public Health England, Birmingham, B3 2PW, UK.

Background: Syndromic surveillance complements traditional public health surveillance by collecting and analysing health indicators in near real time. The rationale of syndromic surveillance is that it may detect health threats faster than traditional surveillance systems permitting more timely, and hence potentially more effective public health action. The effectiveness of syndromic surveillance largely relies on the methods used to detect aberrations. Very few studies have evaluated the performance of syndromic surveillance systems and consequently little is known about the types of events that such systems can and cannot detect.

Methods: We introduce a framework for the evaluation of syndromic surveillance systems that can be used in any setting based upon the use of simulated scenarios. For a range of scenarios this allows the time and probability of detection to be determined and uncertainty is fully incorporated. In addition, we demonstrate how such a framework can model the benefits of increases in the number of centres reporting syndromic data and also determine the minimum size of outbreaks that can or cannot be detected. Here, we demonstrate its utility using simulations of national influenza outbreaks and localised outbreaks of cryptosporidiosis.

Results: Influenza outbreaks are consistently detected with larger outbreaks being detected in a more timely manner. Small cryptosporidiosis outbreaks (<1000 symptomatic individuals) are unlikely to be detected. We also demonstrate the advantages of having multiple syndromic data streams (e.g. emergency attendance data, telephone helpline data, general practice consultation data) as different streams are able to detect different outbreak types with different efficacy (e.g. emergency attendance data are useful for the detection of pandemic influenza but not for outbreaks of cryptosporidiosis). We also highlight that for any one disease, the utility of data streams may vary geographically, and that the detection ability of syndromic surveillance varies seasonally (e.g. an influenza outbreak starting in July is detected sooner than one starting later in the year). We argue that our framework constitutes a useful tool for public health emergency preparedness in multiple settings.

Conclusions: The proposed framework allows the exhaustive evaluation of any syndromic surveillance system and constitutes a useful tool for emergency preparedness and response.
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http://dx.doi.org/10.1186/s12889-018-5422-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5921418PMC
April 2018

After the epidemic: Zika virus projections for Latin America and the Caribbean.

PLoS Negl Trop Dis 2017 Nov 1;11(11):e0006007. Epub 2017 Nov 1.

School of Environmental Sciences, University of East Anglia, Norwich, Norfolk, United Kingdom.

Background: Zika is one of the most challenging emergent vector-borne diseases, yet its future public health impact remains unclear. Zika was of little public health concern until recent reports of its association with congenital syndromes. By 3 August 2017 ∼217,000 Zika cases and ∼3,400 cases of associated congenital syndrome were reported in Latin America and the Caribbean. Some modelling exercises suggest that Zika virus infection could become endemic in agreement with recent declarations from the The World Health Organisation.

Methodology/principal Findings: We produced high-resolution spatially-explicit projections of Zika cases, associated congenital syndromes and monetary costs for Latin America and the Caribbean now that the epidemic phase of the disease appears to be over. In contrast to previous studies which have adopted a modelling approach to map Zika potential, we project case numbers using a statistical approach based upon reported dengue case data as a Zika surrogate. Our results indicate that ∼12.3 (0.7-162.3) million Zika cases could be expected across Latin America and the Caribbean every year, leading to ∼64.4 (0.2-5159.3) thousand cases of Guillain-Barré syndrome and ∼4.7 (0.0-116.3) thousand cases of microcephaly. The economic burden of these neurological sequelae are estimated to be USD ∼2.3 (USD 0-159.3) billion per annum.

Conclusions/significance: Zika is likely to have significant public health consequences across Latin America and the Caribbean in years to come. Our projections inform regional and federal health authorities, offering an opportunity to adapt to this public health challenge.
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http://dx.doi.org/10.1371/journal.pntd.0006007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5683651PMC
November 2017

Assessing the effects of air temperature and rainfall on malaria incidence: an epidemiological study across Rwanda and Uganda.

Geospat Health 2016 Mar 31;11(1 Suppl):379. Epub 2016 Mar 31.

Abdus Salam International Centre for Theoretical Physics, Trieste, Italy; School of Environmental Sciences, University of East Anglia, Norwich.

We investigate the short-term effects of air temperature, rainfall, and socioeconomic indicators on malaria incidence across Rwanda and Uganda from 2002 to 2011. Delayed and nonlinear effects of temperature and rainfall data are estimated using generalised additive mixed models with a distributed lag nonlinear specification. A time series cross-validation algorithm is implemented to select the best subset of socioeconomic predictors and to define the degree of smoothing of the weather variables. Our findings show that trends in malaria incidence agree well with variations in both temperature and rainfall in both countries, although factors other than climate seem to play an important role too. The estimated short-term effects of air temperature and precipitation are nonlinear, in agreement with previous research and the ecology of the disease. These effects are robust to the effects of temporal correlation. The effects of socioeconomic data are difficult to ascertain and require further evaluation with longer time series. Climate-informed models had lower error estimates compared to models with no climatic information in 77 and 60% of the districts in Rwanda and Uganda, respectively. Our results highlight the importance of using climatic information in the analysis of malaria surveillance data, and show potential for the development of climate informed malaria early warning systems.
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http://dx.doi.org/10.4081/gh.2016.379DOI Listing
March 2016

Climate change and the emergence of vector-borne diseases in Europe: case study of dengue fever.

BMC Public Health 2014 Aug 22;14:781. Epub 2014 Aug 22.

Norwich Medical School, University of East Anglia, Norwich, UK.

Background: Dengue fever is the most prevalent mosquito-borne viral disease worldwide. Dengue transmission is critically dependent on climatic factors and there is much concern as to whether climate change would spread the disease to areas currently unaffected. The occurrence of autochthonous infections in Croatia and France in 2010 has raised concerns about a potential re-emergence of dengue in Europe. The objective of this study is to estimate dengue risk in Europe under climate change scenarios.

Methods: We used a Generalized Additive Model (GAM) to estimate dengue fever risk as a function of climatic variables (maximum temperature, minimum temperature, precipitation, humidity) and socioeconomic factors (population density, urbanisation, GDP per capita and population size), under contemporary conditions (1985-2007) in Mexico. We then used our model estimates to project dengue incidence under baseline conditions (1961-1990) and three climate change scenarios: short-term 2011-2040, medium-term 2041-2070 and long-term 2071-2100 across Europe. The model was used to calculate average number of yearly dengue cases at a spatial resolution of 10 × 10 km grid covering all land surface of the currently 27 EU member states. To our knowledge, this is the first attempt to model dengue fever risk in Europe in terms of disease occurrence rather than mosquito presence.

Results: The results were presented using Geographical Information System (GIS) and allowed identification of areas at high risk. Dengue fever hot spots were clustered around the coastal areas of the Mediterranean and Adriatic seas and the Po Valley in northern Italy.

Conclusions: This risk assessment study is likely to be a valuable tool assisting effective and targeted adaptation responses to reduce the likely increased burden of dengue fever in a warmer world.
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http://dx.doi.org/10.1186/1471-2458-14-781DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143568PMC
August 2014

Impact of climate change on global malaria distribution.

Proc Natl Acad Sci U S A 2014 Mar 3;111(9):3286-91. Epub 2014 Feb 3.

Institute of Infection and Global Health, Department of Epidemiology and Population Health and School of Environmental Sciences, Department of Geography and Planning, University of Liverpool, Liverpool L69 7ZT, United Kingdom.

Malaria is an important disease that has a global distribution and significant health burden. The spatial limits of its distribution and seasonal activity are sensitive to climate factors, as well as the local capacity to control the disease. Malaria is also one of the few health outcomes that has been modeled by more than one research group and can therefore facilitate the first model intercomparison for health impacts under a future with climate change. We used bias-corrected temperature and rainfall simulations from the Coupled Model Intercomparison Project Phase 5 climate models to compare the metrics of five statistical and dynamical malaria impact models for three future time periods (2030s, 2050s, and 2080s). We evaluated three malaria outcome metrics at global and regional levels: climate suitability, additional population at risk and additional person-months at risk across the model outputs. The malaria projections were based on five different global climate models, each run under four emission scenarios (Representative Concentration Pathways, RCPs) and a single population projection. We also investigated the modeling uncertainty associated with future projections of populations at risk for malaria owing to climate change. Our findings show an overall global net increase in climate suitability and a net increase in the population at risk, but with large uncertainties. The model outputs indicate a net increase in the annual person-months at risk when comparing from RCP2.6 to RCP8.5 from the 2050s to the 2080s. The malaria outcome metrics were highly sensitive to the choice of malaria impact model, especially over the epidemic fringes of the malaria distribution.
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http://dx.doi.org/10.1073/pnas.1302089111DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3948226PMC
March 2014

Multimodel assessment of water scarcity under climate change.

Proc Natl Acad Sci U S A 2014 Mar 16;111(9):3245-50. Epub 2013 Dec 16.

Potsdam Institute for Climate Impact Research, 14412 Potsdam, Germany.

Water scarcity severely impairs food security and economic prosperity in many countries today. Expected future population changes will, in many countries as well as globally, increase the pressure on available water resources. On the supply side, renewable water resources will be affected by projected changes in precipitation patterns, temperature, and other climate variables. Here we use a large ensemble of global hydrological models (GHMs) forced by five global climate models and the latest greenhouse-gas concentration scenarios (Representative Concentration Pathways) to synthesize the current knowledge about climate change impacts on water resources. We show that climate change is likely to exacerbate regional and global water scarcity considerably. In particular, the ensemble average projects that a global warming of 2 °C above present (approximately 2.7 °C above preindustrial) will confront an additional approximate 15% of the global population with a severe decrease in water resources and will increase the number of people living under absolute water scarcity (<500 m(3) per capita per year) by another 40% (according to some models, more than 100%) compared with the effect of population growth alone. For some indicators of moderate impacts, the steepest increase is seen between the present day and 2 °C, whereas indicators of very severe impacts increase unabated beyond 2 °C. At the same time, the study highlights large uncertainties associated with these estimates, with both global climate models and GHMs contributing to the spread. GHM uncertainty is particularly dominant in many regions affected by declining water resources, suggesting a high potential for improved water resource projections through hydrological model development.
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http://dx.doi.org/10.1073/pnas.1222460110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3948304PMC
March 2014

The effects of weather and climate change on dengue.

PLoS Negl Trop Dis 2013 Nov 14;7(11):e2503. Epub 2013 Nov 14.

The Abdus Salam International Centre for Theoretical Physics, Earth System Physics Section, Trieste, Italy ; Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom ; School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom.

Background: There is much uncertainty about the future impact of climate change on vector-borne diseases. Such uncertainty reflects the difficulties in modelling the complex interactions between disease, climatic and socioeconomic determinants. We used a comprehensive panel dataset from Mexico covering 23 years of province-specific dengue reports across nine climatic regions to estimate the impact of weather on dengue, accounting for the effects of non-climatic factors.

Methods And Findings: Using a Generalized Additive Model, we estimated statistically significant effects of weather and access to piped water on dengue. The effects of weather were highly nonlinear. Minimum temperature (Tmin) had almost no effect on dengue incidence below 5 °C, but Tmin values above 18 °C showed a rapidly increasing effect. Maximum temperature above 20 °C also showed an increasing effect on dengue incidence with a peak around 32 °C, after which the effect declined. There is also an increasing effect of precipitation as it rose to about 550 mm, beyond which such effect declines. Rising access to piped water was related to increasing dengue incidence. We used our model estimations to project the potential impact of climate change on dengue incidence under three emission scenarios by 2030, 2050, and 2080. An increase of up to 40% in dengue incidence by 2080 was estimated under climate change while holding the other driving factors constant.

Conclusions: Our results indicate that weather significantly influences dengue incidence in Mexico and that such relationships are highly nonlinear. These findings highlight the importance of using flexible model specifications when analysing weather-health interactions. Climate change may contribute to an increase in dengue incidence. Rising access to piped water may aggravate dengue incidence if it leads to increased domestic water storage. Climate change may therefore influence the success or failure of future efforts against dengue.
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http://dx.doi.org/10.1371/journal.pntd.0002503DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3828158PMC
November 2013

Climate variability and dengue fever in warm and humid Mexico.

Am J Trop Med Hyg 2011 May;84(5):757-63

Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, Norfolk, UK.

Multiple linear regression models were fitted to look for associations between changes in the incidence rate of dengue fever and climate variability in the warm and humid region of Mexico. Data were collected for 12 Mexican provinces over a 23-year period (January 1985 to December 2007). Our results show that the incidence rate or risk of infection is higher during El Niño events and in the warm and wet season. We provide evidence to show that dengue fever incidence was positively associated with the strength of El Niño and the minimum temperature, especially during the cool and dry season. Our study complements the understanding of dengue fever dynamics in the region and may be useful for the development of early warning systems.
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http://dx.doi.org/10.4269/ajtmh.2011.10-0609DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3083744PMC
May 2011