Graduate trainee, University of Wisconsin-Madison
Climate drivers of annual dengue epidemics in Ecuador
Season is a major determinant of infectious disease rates, including mosquito-borne arboviruses, such as dengue, chikungunya, and Zika. Seasonal patterns of disease are driven by a combination of climate factors, such as temperature or rainfall, and human behavioral time trends, such as school year schedules, holidays, and weekday-weekend patterns. These factors affect both disease rates and healthcare-seeking behavior. Seasonality of mosquito-borne illnesses has been studied in the context of climate, but temporal trends are less well-understood. With 2009—2016 medical record data from patients diagnosed with arboviral illness at two hospitals in rural Ecuador, we used Poisson generalized linear modeling to determine short- and long-term seasonal patterns of mosquito-borne disease, and the effect of climate factors. The most important predictors of illness were annual fluctuations in disease, long-term trends, day of the week, and climate variables. Compared to Tuesday, weekends were the least likely days for arboviral illness to be diagnosed, with 34% (p=0.007) and 40% (p=0.001) less cases reported on Saturday and Sunday, respectively. Seasonal trends showed a single peak during April, with long-term trends showing an overall decrease in diagnoses and suggested inter-epidemic periods every two or three years. Important climate variables included total monthly precipitation (p=0.002), Oceanic Niño Index (p=0.039), interactions between total precipitation and monthly absolute minimum temperature (p=0.002), total precipitation and monthly number of days with precipitation (p=0.019), and three-way interaction between minimum temperature, total precipitation, and precipitation days (p=0.014). Seasonality assessments revealed 10- to 30-day lags between peaks in climate variables and disease. This is the first report of arboviral disease seasonality in Ecuador, one of few reports from rural patients, and one of very few studies utilizing daily disease reports. These results can inform local disease prevention efforts, public health planning, and global or regional models of arboviral disease trends.
Abstract: Research has shown that the classical Stegomyia indices (or "larval indices") of the dengue vector Aedes aegypti reflect the absence or presence of the vector but do not provide accurate measures of adult mosquito density. In contrast, pupal indices as collected in pupal productivity surveys are a much better proxy indicator for adult vector abundance. However, it is unknown when it is most optimal to conduct pupal productivity surveys, in the wet or in the dry season or in both, to inform control services about the most productive water container types and if this pattern varies among different ecological settings.A multi-country study in randomly selected twelve to twenty urban and peri-urban neighborhoods ("clusters") of six Asian countries, in which all water holding containers were examined for larvae and pupae of Aedes aegypti during the dry season and the wet season and their productivity was characterized by water container types. In addition, meteorological data and information on reported dengue cases were collected.The study reconfirmed the association between rainfall and dengue cases ("dengue season") and underlined the importance of determining through pupal productivity surveys the "most productive containers types", responsible for the majority (>70%) of adult dengue vectors. The variety of productive container types was greater during the wet than during the dry season, but included practically all container types productive in the dry season. Container types producing pupae were usually different from those infested by larvae indicating that containers with larval infestations do not necessarily foster pupal development and thus the production of adult Aedes mosquitoes.Pupal productivity surveys conducted during the wet season will identify almost all of the most productive container types for both the dry and wet seasons and will therefore facilitate cost-effective targeted interventions.
Pub.: 16 Jan '13, Pinned: 17 Jun '17
Abstract: Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create accurate and actionable real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created an operational and computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing na\"ive seasonal models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making.
Pub.: 15 Nov '15, Pinned: 17 Jun '17
Abstract: Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics.
Pub.: 26 Feb '16, Pinned: 17 Jun '17
Abstract: A fundamental mystery for dengue and other infectious pathogens is how observed patterns of cases relate to actual chains of individual transmission events. These pathways are intimately tied to the mechanisms by which strains interact and compete across spatial scales. Phylogeographic methods have been used to characterize pathogen dispersal at global and regional scales but have yielded few insights into the local spatiotemporal structure of endemic transmission. Using geolocated genotype (800 cases) and serotype (17,291 cases) data, we show that in Bangkok, Thailand, 60% of dengue cases living <200 meters apart come from the same transmission chain, as opposed to 3% of cases separated by 1 to 5 kilometers. At distances <200 meters from a case (encompassing an average of 1300 people in Bangkok), the effective number of chains is 1.7. This number rises by a factor of 7 for each 10-fold increase in the population of the "enclosed" region. This trend is observed regardless of whether population density or area increases, though increases in density over 7000 people per square kilometer do not lead to additional chains. Within Thailand these chains quickly mix, and by the next dengue season viral lineages are no longer highly spatially structured within the country. In contrast, viral flow to neighboring countries is limited. These findings are consistent with local, density-dependent transmission and implicate densely populated communities as key sources of viral diversity, with home location the focal point of transmission. These findings have important implications for targeted vector control and active surveillance.
Pub.: 25 Mar '17, Pinned: 17 Jun '17
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