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

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

Impact of the shedding level on transmission of persistent infections in Mycobacterium avium subspecies paratuberculosis (MAP)

Super-shedders are infectious individuals that contribute a disproportionate amount of infectious pathogen load to the environment. A super-shedder host may produce up to 10 000 times more pathogens than other infectious hosts. Super-shedders have been reported for multiple human and animal diseases. If their contribution to infection dynamics was linear to the pathogen load, they would dominate infection dynamics. We here focus on quantifying the effect of super-shedders on the spread of infection in natural environments to test if such an effect actually occurs in Mycobacterium avium subspecies paratuberculosis (MAP). We study a case where the infection dynamics and the bacterial load shed by each host at every point in time are known. Using a maximum likelihood approach, we estimate the parameters of a model with multiple transmission routes, including direct contact, indirect contact and a background infection risk. We use longitudinal data from persistent infections (MAP), where infectious individuals have a wide distribution of infectious loads, ranging upward of three orders of magnitude. We show based on these parameters that the effect of super-shedders for MAP is limited and that the effect of the individual bacterial load is limited and the relationship between bacterial load and the infectiousness is highly concave. A 1000-fold increase in the bacterial contribution is equivalent to up to a 2–3 fold increase in infectiousness.https://doi.org/10.1186/s13567-016-0323-

Canine visceral leishmaniasis in Araçatuba, state of São Paulo, Brazil, and its relationship with characteristics of dogs and their owners: a cross-sectional and spatial analysis using a geostatistical approach

BACKGROUND: The incidence of visceral leishmaniasis (VL), one of the most important neglected diseases worldwide, is increasing in Brazil. The objectives of this study were to determine the canine VL (CanL) seroprevalence in an urban area of Araçatuba municipality and to evaluate its relationship with the characteristics of dogs and their owners. RESULTS: The CanL seroprevalence in the study area was 0.081 (95% credible interval [CI]: 0.068-0.096). The following covariates/categories were positively associated with the occurrence of a seropositive dog: more than 10 dogs that had lived in the house (odds ratio [OR] = 2.36; 95% CI: 1.03-5.43) (baseline: 0-10 dogs); house with dogs that previously died of VL (OR = 4.85; 95% CI: 2.65-8.86) or died of causes other than old age (OR = 2.26; 95% CI: 1.12-4.46) (baseline: natural or no deaths); dogs that spent the day in a sheltered backyard (OR = 2.14; 95% CI: 1.05-4.40); dogs that spent the day in an unsheltered backyard or the street (OR = 2.67; 95% CI: 1.28-5.57) (baseline: inside home). Spatial dependence among observations occurred within about 45.7 m. CONCLUSIONS: The number of dogs that had lived in the house, previous deaths by VL or other cause, and the place the dog stayed during the day were associated with the occurrence of a VL seropositive dog. The short-distance spatial dependence could be related to the vector characteristics, producing a local neighbourhood VL transmission pattern. The geostatistical approach in a Bayesian context using integrated nested Laplace approximation (INLA) allowed to identify the covariates associated with VL, including its spatially dependent transmission pattern

The Role of GIS, Mobile App and Satellite Technologies to Enhance Data Collection Process: A Case of Environmental Factors and Epidemics Linkages

A research article was submitted to Smart Cities/Smart Regions – Technische, wirtschaftliche und gesellschaftliche Innovationen, 2019Enhancement of health data collection and presentation to support epidemic analysis can benefit many aspects of healthcare in terms of diseases control, decision making and action to be taken. The epidemic analysis is the science that studies the patterns, causes, and effects of health and diseases conditions in defined populations. In most hospitals, there is increasing demand to improve quality of data, the efficiency of collection and presentation. In this study, we aim at integrating new module in the existing Health Information System (HIS) in order to improve data collection and presentation. The module takes advantage of the emerging technologies of mobile application, satellite technology and Geographical Information System (GIS) to capture environmental data. As part of the module we have developed, the mobile app which is integrated with GIS and satellite technology for remote data collection and hence the module can play a vital role in enhancing epidemic analysis