Spatial Distribution of Phlebotomus argentipes (Diptera: Psychodidae) in Eastern India, a Case Study Evaluating Multispatial Resolution Remotely Sensed Environmental Evidence and Microclimatic Data

Assessing the combined effects of household type and insecticide effectiveness for kala-azar vector control using indoor residual spraying: a case study from North Bihar, India

BACKGROUND

Indoor residual spraying (IRS) is the mainstay for vector control intervention of visceral leishmaniasis (VL) in India. Little is known on the control effects of IRS on different household types. Here, we assessed if IRS with insecticides has an equal residual and interventional effect on all household types in a village. We also developed a combined spatial-risk map and a sand fly, Phlebotomus argentipes density analytical model based on household characteristics, insecticide susceptibility and IRS-status to explore the spatio-temporal distributions of the vector at a micro-scale level.

METHODS

This study was carried out in two villages of Mahnar block in Vaishali district, Bihar. IRS using two insecticides [dichlorodiphenyltrichloroethane (DDT 50%) and synthetic pyrethroid (SP 5%)] was evaluated for VL-vector (P. argentipes) control. Temporal residual efficacy of the insecticides on different wall-surface types was evaluated using the cone-bioassay technique according to WHO guidelines. Insecticide susceptibility of local P. argentipes was explored using the tube-bioassay method. Pre- and post-IRS sand fly densities were monitored in human dwellings and animal shelters using Centers for Disease Control light-traps installed between 18:00-6:00 h. A best-fit model for sand fly density analysis was developed using multiple logistic regression analysis. Geographical information system based spatial analysis techniques were employed to map the household type distribution of insecticide susceptibility of the vector, and IRS-status of the households to interpret the spatio-temporal distributions of P. argentipes.

RESULTS

Phlebotomus argentipes was highly susceptible to SP (100%) but showed high resistance to DDT with a 49.1% mortality rate. SP-IRS has been reported as having better community acceptance than DDT-IRS in all household types. Residual efficacies were varied between wall-surfaces; both insecticides failed to achieve the duration of IRS effectiveness recommended by the WHO. Reduction in P. argentipes counts due to SP-IRS was higher than DDT-IRS between household groups (i.e. sprayed and sentinel), in all intervals post-IRS. Combined spatial risk-maps revealed a better control effect of SP-IRS on sand flies than DDT-IRS in all household types risk-zones. The multilevel logistic regression analysis explored five risk-factors that were strongly associated with the density of P. argentipes.

CONCLUSIONS

The results contribute to furthering current understanding of IRS-practices for control of visceral leishmaniasis in endemic Bihar, which may help in future actions for improvements.

Impact of past and on-going changes on climate and weather on vector-borne diseases transmission: a look at the evidence

Background

The climate variables that directly influence vector-borne diseases’ ecosystems are mainly temperature and rainfall. This is not only because the vectors bionomics are strongly dependent upon these variables, but also because most of the elements of the systems are impacted, such as the host behavior and development and the pathogen amplification. The impact of the climate changes on the transmission patterns of these diseases is not easily understood, since many confounding factors are acting together. Consequently, knowledge of these impacts is often based on hypothesis derived from mathematical models. Nevertheless, some direct evidences can be found for several vector-borne diseases.

Main body

Evidences of the impact of climate change are available for malaria, arbovirus diseases such as dengue, and many other parasitic and viral diseases such as Rift Valley Fever, Japanese encephalitis, human African trypanosomiasis and leishmaniasis. The effect of temperature and rainfall change as well as extreme events, were found to be the main cause for outbreaks and are alarming the global community. Among the main driving factors, climate strongly influences the geographical distribution of insect vectors, which is rapidly changing due to climate change. Further, in both models and direct evidences, climate change is seen to be affecting vector-borne diseases more strikingly in fringe of different climatic areas often in the border of transmission zones, which were once free of these diseases with human populations less immune and more receptive. The impact of climate change is also more devastating because of the unpreparedness of Public Health systems to provide adequate response to the events, even when climatic warning is available. Although evidences are strong at the regional and local levels, the studies on impact of climate change on vector-borne diseases and health are producing contradictory results at the global level.

Conclusions

In this paper we discuss the current state of the results and draw on evidences from malaria, dengue and other vector-borne diseases to illustrate the state of current thinking and outline the need for further research to inform our predictions and response.

Electronic supplementary material

The online version of this article (10.1186/s40249-019-0565-1) contains supplementary material, which is available to authorized users.

The role of case proximity in transmission of visceral leishmaniasis in a highly endemic village in Bangladesh

BACKGROUND

Visceral leishmaniasis (VL) is characterised by a high degree of spatial clustering at all scales, and this feature remains even with successful control measures. VL is targeted for elimination as a public health problem in the Indian subcontinent by 2020, and incidence has been falling rapidly since 2011. Current control is based on early diagnosis and treatment of clinical cases, and blanket indoor residual spraying of insecticide (IRS) in endemic villages to kill the sandfly vectors. Spatially targeting active case detection and/or IRS to higher risk areas would greatly reduce costs of control, but its effectiveness as a control strategy is unknown. The effectiveness depends on two key unknowns: how quickly transmission risk decreases with distance from a VL case and how much asymptomatically infected individuals contribute to transmission.

METHODOLOGY/PRINCIPAL FINDINGS

To estimate these key parameters, a spatiotemporal transmission model for VL was developed and fitted to geo-located epidemiological data on 2494 individuals from a highly endemic village in Mymensingh, Bangladesh. A Bayesian inference framework that could account for the unknown infection times of the VL cases, and missing symptom onset and recovery times, was developed to perform the parameter estimation. The parameter estimates obtained suggest that, in a highly endemic setting, VL risk decreases relatively quickly with distance from a case-halving within 90m-and that VL cases contribute significantly more to transmission than asymptomatic individuals.

CONCLUSIONS/SIGNIFICANCE

These results suggest that spatially-targeted interventions may be effective for limiting transmission. However, the extent to which spatial transmission patterns and the asymptomatic contribution vary with VL endemicity and over time is uncertain. In any event, interventions would need to be performed promptly and in a large radius (≥300m) around a new case to reduce transmission risk.

Trends in spatio-temporal dynamics of visceral leishmaniasis cases in a highly-endemic focus of Bihar, India: an investigation based on GIS tools

Background

Visceral leishmaniasis (VL) in Bihar State (India) continues to be endemic, despite the existence of effective treatment and a vector control program to control disease morbidity. A clear understanding of spatio-temporal distribution of VL may improve surveillance and control implementation. This study explored the trends in spatio-temporal dynamics of VL endemicity at a meso-scale level in Vaishali District, based on geographical information systems (GIS) tools and spatial statistical analysis.

Methods

A GIS database was used to integrate the VL case data from the study area between 2009 and 2014. All cases were spatially linked at a meso-scale level. Geospatial techniques, such as GIS-layer overlaying and mapping, were employed to visualize and detect the spatio-temporal patterns of a VL endemic outbreak across the district. The spatial statistic Moran’s I Index (Moran’s I) was used to simultaneously evaluate spatial-correlation between endemic villages and the spatial distribution patterns based on both the village location and the case incidence rate (CIR). Descriptive statistics such as mean, standard error, confidence intervals and percentages were used to summarize the VL case data.

Results

There were 624 endemic villages with 2719 (average 906 cases/year) VL cases during 2012–2014. The Moran’s I revealed a cluster pattern (P < 0.05) of CIR distribution at the meso-scale level. On average, 68 villages were newly-endemic each year. Of which 93.1% of villages’ endemicity were found to have occurred on the peripheries of the previous year endemic villages. The mean CIR of the endemic villages that were peripheral to the following year newly-endemic villages, compared to all endemic villages of the same year, was higher (P < 0.05).

Conclusion

The results show that the VL endemicity of new villages tends to occur on the periphery of villages endemic in the previous year. High-CIR plays a major role in the spatial dispersion of the VL cases between non-endemic and endemic villages. This information can help achieve VL elimination throughout the Indian subcontinent by improving vector control design and implementation in highly-endemic district.