As of 2021, the National Kala-azar Elimination Programme (NKAEP) in India has achieved visceral leishmaniasis (VL) elimination (<1 case / 10,000 population/year per block) in 625 of the 633 endemic ...blocks (subdistricts) in four states. The programme needs to sustain this achievement and target interventions in the remaining blocks to achieve the WHO 2030 target of VL elimination as a public health problem. An effective tool to analyse programme data and predict/ forecast the spatial and temporal trends of VL incidence, elimination threshold, and risk of resurgence will be of use to the programme management at this juncture.
We employed spatiotemporal models incorporating environment, climatic and demographic factors as covariates to describe monthly VL cases for 8-years (2013-2020) in 491 and 27 endemic and non-endemic blocks of Bihar and Jharkhand states. We fitted 37 models of spatial, temporal, and spatiotemporal interaction random effects with covariates to monthly VL cases for 6-years (2013-2018, training data) using Bayesian inference via Integrated Nested Laplace Approximation (INLA) approach. The best-fitting model was selected based on deviance information criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) and was validated with monthly cases for 2019-2020 (test data). The model could describe observed spatial and temporal patterns of VL incidence in the two states having widely differing incidence trajectories, with >93% and 99% coverage probability (proportion of observations falling inside 95% Bayesian credible interval for the predicted number of VL cases per month) during the training and testing periods. PIT (probability integral transform) histograms confirmed consistency between prediction and observation for the test period. Forecasting for 2021-2023 showed that the annual VL incidence is likely to exceed elimination threshold in 16-18 blocks in 4 districts of Jharkhand and 33-38 blocks in 10 districts of Bihar. The risk of VL in non-endemic neighbouring blocks of both Bihar and Jharkhand are less than 0.5 during the training and test periods, and for 2021-2023, the probability that the risk greater than 1 is negligible (P<0.1). Fitted model showed that VL occurrence was positively associated with mean temperature, minimum temperature, enhanced vegetation index, precipitation, and isothermality, and negatively with maximum temperature, land surface temperature, soil moisture and population density.
The spatiotemporal model incorporating environmental, bioclimatic, and demographic factors demonstrated that the KAMIS database of the national programmme can be used for block level predictions of long-term spatial and temporal trends in VL incidence and risk of outbreak / resurgence in endemic and non-endemic settings. The database integrated with the modelling framework and a dashboard facility can facilitate such analysis and predictions. This could aid the programme to monitor progress of VL elimination at least one-year ahead, assess risk of resurgence or outbreak in post-elimination settings, and implement timely and targeted interventions or preventive measures so that the NKAEP meet the target of achieving elimination by 2030.
Seawater samples at 54 stations in the year 2011–2012 from Chidiyatappu, Port Blair, Rangat and Aerial Bays of Andaman Sea, have been investigated in the present study. Datasets obtained have been ...converted into simple maps using coastal water quality index (CWQI) and Geographical Information System (GIS) based overlay mapping technique to demarcate healthy and polluted areas. Analysis of multiple parameters revealed poor water quality in Port Blair and Rangat Bays. The anthropogenic activities may be the likely cause for poor water quality. Whereas, good water quality was witnessed at Chidiyatappu Bay. Higher CWQI scores were perceived in the open sea. However, less exploitation of coastal resources owing to minimal anthropogenic activity indicated good water quality index at Chidiyatappu Bay. This study is an attempt to integrate CWQI and GIS based mapping technique to derive a reliable, simple and useful output for water quality monitoring in coastal environment.
•Poor water quality observed in Port Blair and Rangat Bays•Index value showed good water quality in Chidiyatappu.•Higher index scores are witnessed in the open sea rather than the inner bays.•CWQI and GIS proved to be a simple and effective tool for water quality monitoring.
Maps show well the spatial configuration of information. Considerable effort is devoted to the development of geographical information systems (GIS) that increase understanding of public health ...problems and in particular to collaborate efforts among clinicians, epidemiologists, ecologists, and geographers to map and forecast disease risk.
Small populations tend to give rise to the most extreme disease rates, even if the actual rates are similar across the areas. Such situations will follow the decision-maker's attention on these areas when they scrutinize the map for decision making or resource allocation. As an alternative, maps can be prepared using P-values (probabilistic values).
The statistical significance of rates rather than the rates themselves are used to map the results. The incidence rates calculated for each village from 2000 to 2009 is used to estimate λ, the expected number of cases in the study area. The obtained results are mapped using Arc GIS 10.0.
The likelihood of infections from low to high is depicted in the map and it is observed that five villages namely, Odanthurai, Coimbatore Corporation, Ikkaraiboluvampatti, Puliakulam, and Pollachi Corporation are more likely to have significantly high incidences.
In the probability map, some of the areas with exceptionally high or low rates disappear. These are typically small unpopulated areas, whose rates are unstable due to the small numbers problem. The probability map shows more specific regions of relative risks and expected outcomes.
Drinking Water Quality is a powerful environmental determinant of human health, especially for children. The present study is undertaken to assess the drinking water quality in schools of ...Tiruchirappalli region and to estimate the impact of land use stress on water quality. Drinking water samples have been collected from 102 schools of Tiruchirappalli during October 2011. The physico-chemical and microbial parameters of the drinking water were analyzed. The drinking Water Quality Index has been derived for the estimated parameters and the water is assessed as Excellent, Good, Medium, Poor, and Very Poor. The estimated DWQI shows that more samples fall under very poor (68) and bad category (22) and one infers that around 83 percent of the water samples were unsuitable for drinking purposes, 12 percent of the samples shows moderate water quality and only 5 percent of samples was observed to be suitable for drinking purposes. Based on the derived DWQI a unique symbol map was prepared. Land use/land cover map was derived for the study area using IRS LISS III images acquired during 2009. The DWQI map was superimposed on the land use/land cover map to identify the problem areas and the extent of deterioration. Through this study the land use stress on water quality can be quantified and appropriate recommendations may be suggested to the school authorities for the proper management of water quality so as to sustain a healthy life of the children.
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