Contact rates between humans and mosquitoes also vary considerably among locations due to the influence of meteorological variables, such as temperature, rainfall, and relative humidity, on mosquito population dynamics. Over large spatial scales, e.g., at the national or provincial scale, human populations are unevenly distributed, and population mixing and movement depend on transportation networks, with movement among localities affected by a number of different economic, cultural, geographical, and environmental factors. Empirical studies have shown that chikungunya clusters at scales of neighborhoods or villages, implying that models posed at larger scales may be incompatible with the biology of CHIKV transmission. The spatial scale at which this assumption is reasonable is determined primarily by the scales of both human and mosquito movement. One important assumption that is often made is that the human population is well mixed, which for a mosquito-transmitted pathogen means that each person within a given area has an equal chance of being bitten by any of the mosquitoes within that area. For simplicity, these models often make assumptions about transmission dynamics that do not reflect biological reality.
#Multipatch couldnt read patch input series#
Models are often fitted to time series of confirmed or suspected cases to estimate epidemiological parameters such as the reproduction number of the pathogen, which can be used to predict how rapidly the epidemic will spread or whether it is expected to die out. Mathematical models can play a critical role in identifying at-risk populations and forecasting the course of an epidemic based on current epidemiological conditions. In addition, public health officials would like to be able to predict where epidemics of these diseases may spread next.
Faced with these large epidemics, limited resources need to be targeted towards areas with the highest transmission and the most vulnerable populations. Due to the influence of environmental conditions on DENV transmission, as well as complex immunological interactions among the four DENV serotypes, many regions experience periodic dengue epidemics. In addition, hundreds of millions of people are infected by dengue virus (DENV) each year. In the past 5 years, both the Zika (ZIKV) and chikungunya (CHIKV) viruses were introduced into the Western Hemisphere and rapidly spread among naïve populations in South America, Central America, and the Caribbean, resulting in millions of cases and causing a public health crisis. Viral diseases transmitted by mosquitoes, including dengue, Zika, chikungunya, and yellow fever, are a rapidly growing problem and together pose a risk to approximately half the world’s population. Black dots represent the observed time series, darker colored lines are the single best-fitting simulations, and lighter colored lines are the other 40 top simulations. Comparisons of department-level results for single-patch and multi-patch models for three different symptomatic rates (0.54, 0.72, and 0.90). Each figure contains results from four departments, with the departments ordered from lowest to highest relative MASE as displayed in Fig. Left panels are estimates from the single-patch departmental model, and right panels are estimated from the multi-patch departmental model. The joint distribution of parameter estimates for the timing of the initial importation event(s) and the magnitude of importation.
The joint distribution of parameter estimates for amount of rainfall-associated temporary larval mosquito habitat and the decay rate of that temporary habitat. Solid lines are means and shaded areas represent the range. (B) Epidemic time series for three different maximum adaptive sampling population sizes. Dashed line represents the mean, and the dotted lines are the mean ± the standard deviation. (A) Cumulative incidence as a function of the maximum adaptive sampling population size.
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