For the past ten years, the number of dengue cases has gradually increased in India. Dengue is driven by complex interactions among host, vector and virus that are influenced by climatic factors. In the present study, we focused on the extrinsic incubation period (EIP) and its variability in different climatic zones of India. The EIP was calculated by using daily and monthly mean temperatures for the states of Punjab, Haryana, Gujarat, Rajasthan and Kerala. Among the studied states, a faster/low EIP in Kerala (8–15 days at 30.8 and 23.4 °C) and a generally slower/high EIP in Punjab (5.6–96.5 days at 35 and 0 °C) were simulated with daily temperatures. EIPs were calculated for different seasons, and Kerala showed the lowest EIP during the monsoon period. In addition, a significant association between dengue cases and precipitation was also observed. The results suggest that temperature is important in virus development in different climatic regions and may be useful in understanding spatio-temporal variations in dengue risk. Climate-based disease forecasting models in India should be refined and tailored for different climatic zones, instead of use of a standard model.

Dengue is a widespread vector-borne disease believed to affect between 100 and 390 million people every year. The interaction between vector, host and pathogen is influenced by various climatic factors and the relationship between dengue and climatic conditions has been poorly explored in India. This study explores the relationship between El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and dengue cases in India. Additionally, distributed lag non-linear model was used to assess the delayed effects of climatic factors on dengue cases. The weekly dengue cases reported by the Integrated Disease Surveillance Program (IDSP) over India during the period 2010–2017 were analysed. The study shows that dengue cases usually follow a seasonal pattern, with most cases reported in August and September. Both temperature and rainfall were positively associated with the number of dengue cases. The precipitation shows the higher transmission risk of dengue was observed between 8 and 15 weeks of lag. The highest relative risk (RR) of dengue was observed at 60 mm rainfall with a 12-week lag period when compared with 40 and 80 mm rainfall. The RR of dengue tends to increase with increasing mean temperature above 24 °C. The largest transmission risk of dengue was observed at 30 °C with a 0–3 weeks of lag. Similarly, the transmission risk increases more than twofold when the minimum temperature reaches 26 °C with a 2-week lag period. The dengue cases and El Niño were positively correlated with a 3–6 months lag period. The significant correlation observed between the IOD and dengue cases was shown for a 0–2 months lag period.

BackgroundTo assess the impact of socioeconomic variables on lymphatic filariasis in endemic villages of Karimnagar district, Andhra Pradesh, India.MethodsA pilot scale study was conducted in 30 villages of Karimnagar district from 2004 to 2007. These villages were selected based on previous reports from department of health, Government of Andhra Pradesh, epidemiology, entomology and socioeconomic survey was conducted as per protocol. Collected data were analysed statistically by Chi square test, Principal Component Analysis, Odds ratio, Bivariate, multivariate logistic regression analysis.ResultsTotal of 5,394 blood samples collected and screened for microfilaria, out of which 199 were found to be positive (3.7%). The socioeconomic data of these respondents/participants were correlated with MF prevalence. The socioeconomic variables like educational status (Odds Ratio (OR) = 2.6, 95% Confidence Interval (CI) = 1.1–6.5), house structure (hut OR = 1.9, 95% CI = 1.2–3.1; tiled OR = 1.3, 95% CI = 0.8–2) and participation in mass drug administration program (OR = 1.8, 95% CI = 1.3–2.6) were found to be highly associated with the occurrence of filarial disease. The socioeconomic index was categorized into low (3.6%; OR-1.1, 95% CI: 0.7–1.5) medium (4.9%; OR-1.5, 95% CI = 1–2.1) and high (3.3%) in relation to percentage of filarial parasite prevalence. A significant difference was observed among these three groups while comparing the number of cases of filaria with the type of socioeconomic conditions of the respondents (P = 0.067).ConclusionsFrom this study it is inferred that age, education of family, type of house structure and awareness about the filarial disease directly influenced the disease prevalence. Beside annual mass drug administration program, such type of analysis should be undertaken by health officials to target a few socioeconomic factors to reduce the disease burden. Health education campaigns in the endemic villages and imparting of protection measures against mosquitoes using insecticide treated bed nets would substantially reduce the disease in these villages.

Filariasis is one of the major public health concerns in India. Approximately 600 million people spread across 250 districts of India are at risk of filariasis. To predict this disease, a pilot scale study was carried out in 30 villages of Karimnagar district of Telangana from 2004 to 2007 to collect epidemiological and socio-economic data. The collected data are analysed by employing various machine learning techniques such as Naïve Bayes (NB), logistic model tree, probabilistic neural network, J48 (C4.5), classification and regression tree, JRip and gradient boosting machine. The performances of these algorithms are reported using sensitivity, specificity, accuracy and area under ROC curve (AUC). Among all employed classification methods, NB yielded the best AUC of 64% and was equally statistically significant with the rest of the classifiers. Similarly, the J48 algorithm generated 23 decision rules that help in developing an early warning system to implement better prevention and control efforts in the management of filariasis.

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