Forcing Versus Feedback: Epidemic Malaria and Monsoon Rains in Northwest India

Imperial College London, United Kingdom
PLoS Computational Biology (Impact Factor: 4.62). 09/2010; 6(9):e1000898. DOI: 10.1371/journal.pcbi.1000898
Source: PubMed


Malaria epidemics in regions with seasonal windows of transmission can vary greatly in size from year to year. A central question has been whether these interannual cycles are driven by climate, are instead generated by the intrinsic dynamics of the disease, or result from the resonance of these two mechanisms. This corresponds to the more general inverse problem of identifying the respective roles of external forcings vs. internal feedbacks from time series for nonlinear and noisy systems. We propose here a quantitative approach to formally compare rival hypotheses on climate vs. disease dynamics, or external forcings vs. internal feedbacks, that combines dynamical models with recently developed, computational inference methods. The interannual patterns of epidemic malaria are investigated here for desert regions of northwest India, with extensive epidemiological records for Plasmodium falciparum malaria for the past two decades. We formulate a dynamical model of malaria transmission that explicitly incorporates rainfall, and we rely on recent advances on parameter estimation for nonlinear and stochastic dynamical systems based on sequential Monte Carlo methods. Results show a significant effect of rainfall in the inter-annual variability of epidemic malaria that involves a threshold in the disease response. The model exhibits high prediction skill for yearly cases in the malaria transmission season following the monsoonal rains. Consideration of a more complex model with clinical immunity demonstrates the robustness of the findings and suggests a role of infected individuals that lack clinical symptoms as a reservoir for transmission. Our results indicate that the nonlinear dynamics of the disease itself play a role at the seasonal, but not the interannual, time scales. They illustrate the feasibility of forecasting malaria epidemics in desert and semi-arid regions of India based on climate variability. This approach should be applicable to malaria in other locations, to other infectious diseases, and to other nonlinear systems under forcing.

  • Source
    • "This tells us that rainfall may not always be a useful predictor and that mosquitoes can be plentiful even without rainfall. Based on the discussion above, it would not be unusual that the use of rainfall as a covariate in logistic regression models even indicates a negative correlation, which is not always the case with other studies analyzing environmental factors in relation to malaria transmission (Briët et al., 2008; Laneri et al., 2010). Thus, in areas where larval breeding occurs in the absence of rainfall, other environmental proxies will have to be used to identify and map potential breeding sites. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Many entomological studies have analyzed remotely sensed data to assess the relationship between malaria vector distribution and the associated environmental factors. However, the high cost of remotely sensed products with high spatial resolution has often resulted in analyses being conducted at coarse scales using open-source, archived remotely sensed data. In the present study, spatial prediction of potential breeding sites based on multi-scale remotely sensed information in conjunction with entomological data with special reference to presence or absence of larvae was realized. Selected water bodies were tested for mosquito larvae using the larva scooping method, and the results were compared with data on land cover, rainfall, land surface temperature (LST) and altitude presented with high spatial resolution. To assess which environmental factors best predict larval presence or absence, Decision Tree methodology and logistic regression techniques were applied. Both approaches showed that some environmental predictors can reliably distinguish between the two alternatives (existence and non-existence of larvae). For example, the results suggest that larvae are mainly present in very small water pools related to human activities, such as subsistence farming that were also found to be the major determinant for vector breeding. Rainfall, LST and altitude , on the other hand, were less useful as a basis for mapping the distribution of breeding sites. In conclusion, we found that models linking presence of larvae with high-resolution land use have good predictive ability of identifying potential breeding sites.
    Full-text · Article · Jun 2015 · Geospatial health
  • Source
    • "The abiotic environment is a covariate that has to be taken into account in the bivariate relationship between pathogens and their host species (Bourke 1970; Warren & Mordecai 2010). On the one hand, favourable conditions, such as temporarily high humidity (Bourke 1970; Laneri et al. 2010) and warm temperatures (Tainter & Baker 1996; Gutknecht, Field & Balser 2012), have been found to drive pathogen development , reproduction and persistence (Warren & Mordecai 2010). On the other hand, climatic extremes (e.g. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The degree to which plant pathogen infestation occurs in a host plant is expected to be strongly influenced by the level of species diversity among neighbouring host and non-host plant species. Since pathogen infestation can negatively affect host plant performance, it can mediate the effects of local biodiversity on ecosystem functioning. We tested the effects of tree diversity and the proportion of neighbouring host and non-host species with respect to the foliar fungal pathogens of Tilia cordata and Quercus petraea in the Kreinitz tree diversity experiment in Germany. We hypothesized that fungal pathogen richness increases while infestation decreases with increasing local tree diversity. In addition, we tested whether fungal pathogen richness and infestation are dependent on the proportion of host plant species present or on the proportion of particular non-host neighbouring tree species. Leaves of the two target species were sampled across three consecutive years with visible foliar fungal pathogens on the leaf surface being identified macro- and microscopically. Effects of diversity among neighbouring trees were analysed: (i) for total fungal species richness and fungal infestation on host trees and (ii) for infestation by individual fungal species. We detected four and five fungal species on T. cordata and Q. petraea, respectively. High local tree diversity reduced (i) total fungal species richness and infestation of T. cordata and fungal infestation of Q. petraea and (ii) infestation by three host-specialized fungal pathogen species. These effects were brought about by local tree diversity and were independent of host species proportion. In general, host species proportion had almost no effect on fungal species richness and infestation. Strong effects associated with the proportion of particular non-host neighbouring tree species on fungal species richness and infestation were, however, recorded. Synthesis. For the first time, we experimentally demonstrated that for two common forestry tree species, foliar fungal pathogen richness and infestation depend on local biodiversity. Thus, local tree diversity can have positive impacts on ecosystem functioning in managed forests by decreasing the level of fungal pathogen infestation.
    Full-text · Article · Oct 2014 · Journal of Ecology
  • Source
    • "Moreover, λ i = β(t)(I i (t) + I * i (t)) α /P(t) + ω, where 0 ≤ β(t) is parameterized with a trend and a smooth seasonal component, 0 ≤ ω models infections from an environmental reservoir and 0 ≤ α ≤ 1 captures inhomogeneous mixing of the population. Laneri et al., 2010). The role of stochastic environments has also been studied in the context of Markov counting systems, both paying attention to the system probabilistic properties (e.g., Bretó et al., 2009; Marion and Renshaw, 2000; Varughese and Fatti, 2008) and focusing on the biological implications for applications (e.g., Shrestha et al., 2011). "
    [Show abstract] [Hide abstract]
    ABSTRACT: We provide transition rates for Markov counting systems subject to correlated environmental noises motivated by multi-strain disease models. Such noises induce simultaneous counts, which can help model infinitesimal count correlation (regardless of whether such correlation is due to correlated noises).
    Preview · Article · Dec 2013
Show more