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Experiments and models suggest that climate affects mosquito-borne disease transmission. However, disease transmission involves complex nonlinear interactions between climate and population dynamics, which makes detecting climate drivers at the population level challenging. By analysing incidence data, estimated susceptible population size, and climate data with methods based on nonlinear time series analysis (collectively referred to as empirical dynamic modelling), we identified drivers and their interactive effects on dengue dynamics in San Juan, Puerto Rico. Climatic forcing arose only when susceptible availability was high: temperature and rainfall had net positive and negative effects respectively. By capturing mechanistic, nonlinear and context-dependent effects of population susceptibility, temperature and rainfall on dengue transmission empirically, our model improves forecast skill over recent, state-of-the-art models for dengue incidence. Together, these results provide empirical evidence that the interdependence of host population susceptibility and climate drives dengue dynamics in a nonlinear and complex, yet predictable way.
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Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents
Abstract Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases, such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28–85% for vectors, 44–88% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.
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- Award ID(s):
- 2011147
- PAR ID:
- 10289510
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Experiments and models suggest that climate affects mosquito‐borne disease transmission. However, disease transmission involves complex nonlinear interactions between climate and population dynamics, which makes detecting climate drivers at the population level challenging. By analysing incidence data, estimated susceptible population size, and climate data with methods based on nonlinear time series analysis (collectively referred to as empirical dynamic modelling), we identified drivers and their interactive effects on dengue dynamics in San Juan, Puerto Rico. Climatic forcing arose only when susceptible availability was high: temperature and rainfall had net positive and negative effects respectively. By capturing mechanistic, nonlinear and context‐dependent effects of population susceptibility, temperature and rainfall on dengue transmission empirically, our model improves forecast skill over recent, state‐of‐the‐art models for dengue incidence. Together, these results provide empirical evidence that the interdependence of host population susceptibility and climate drives dengue dynamics in a nonlinear and complex, yet predictable way.more » « less
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Abstract How individuals use space and, thus, the rate and the nature of their interactions with others are shaped by their environment. Exogenous changes that alter aggregation patterns, such as resource pulses, can therefore have a significant impact on seemingly unrelated processes like disease spread. White-tailed deer (Odocoileus virginianus) aggregate in oak forests during mast events, and chronic wasting disease (CWD) transmission patterns vary with deer density, so we hypothesize a link between the masting cycle and CWD dynamics. We investigate various possible effects of masting on deer, including shifts to more frequency-dependent CWD transmission due to aggregation, as well as elevated fecundity and decreased mortality of deer in response to the resource pulse, using a simplified compartment model of CWD spread. When masting affects epidemiological parameters, including the strength of frequency dependence in CWD transmission, disease spread during masting events significantly reduces the size of deer populations but, paradoxically, without any change in the proportion of the population in the CWD-diseased state. In contrast, demographic parameters were found in principle to be capable of altering both population size and disease incidence, though the observed effects were very small. While our quantitative findings should be validated using more detailed models of CWD transmission before they are taken as specific predictions about this system, our fundamental qualitative result appears to be quite general. That is, our conclusion that epidemiological rates only influence population size, but demographic rates may affect both population size and disease incidence, can be derived not only from the model we studied but also from classical epidemiological models as well. Our work extends the understanding of the far-reaching impacts of resource pulses through ecological communities by highlighting the vastly different consequences of the same resource pulse acting in different ways.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41467-021-21496-7
