Bio-eco-social determinants of Aedes breeding in field practice area of a medical college in Pune, Maharashtra

Spatiotemporal epidemiology and forecasting of dengue in the state of Punjab, India: Study protocol

Dengue burden in India is a major public health problem. The present study has been designed to understand mechanisms by which routine data generate evidence. Secondary data analysis of routine datasets to understand spatiotemporal epidemiology and forecast dengue will be conducted. Data science approach will be adopted to generate a reproducible framework in the R environment. The lab-confirmed dengue reported by the state health authorities from 01 January 2015 to 31 December 2019 will be included. Multiple climatic variables from satellite imagery, climatic models, vegetation and built-up indices, and sociodemographic variables will be explored as risk factors. Exploratory data analysis followed by statistical analysis and machine learning will be performed. Data analysis will include geospatial information analysis, time series analysis, and spatiotemporal analysis. The study will provide value addition to the existing disease surveillance mechanisms by developing a framework for incorporating multiple routine data sources available in the country.

Yellow fever in Asia-a risk analysis

BACKGROUND

There is concern about the risk of yellow fever (YF) establishment in Asia, owing to rising numbers of urban outbreaks in endemic countries and globalisation. Following an outbreak in Angola in 2016, YF cases were introduced into China. Prior to this, YF had never been recorded in Asia, despite climatic suitability and the presence of mosquitoes. An outbreak in Asia could result in widespread fatalities and huge economic impact. Therefore, quantifying the potential risk of YF outbreaks in Asia is a public health priority.

METHODS

Using international flight data and YF incidence estimates from 2016, we quantified the risk of YF introduction via air travel into Asia. In locations with evidence of a competent mosquito population, the potential for autochthonous YF transmission was estimated using a temperature-dependent model of the reproduction number and a branching process model assuming a negative binomial distribution.

RESULTS

In total, 25 cities across Asia were estimated to be at risk of receiving at least one YF viraemic traveller during 2016. At their average temperatures, we estimated the probability of autochthonous transmission to be <50% in all cities, which was primarily due to the limited number of estimated introductions that year.

CONCLUSION

Despite the rise in air travel, we found low support for travel patterns between YF endemic countries and Asia resulting in autochthonous transmission during 2016. This supports the historic absence of YF in Asia and suggests it could be due to a limited number of introductions in previous years. Future increases in travel volumes or YF incidence can increase the number of introductions and the risk of autochthonous transmission. Given the high proportion of asymptomatic or mild infections and the challenges of YF surveillance, our model can be used to estimate the introduction and outbreak risk and can provide useful information to surveillance systems.