Background and objectivesDengue is an emerging and re-emerging infectious disease, transmitted by mosquitoes. It is mostly prevalent in tropical and sub-tropical regions of the world, particularly, in Asia-Pacific region. To understand the epidemiology and spatial distribution of dengue, a retrospective surveillance study was conducted in the state of Andhra Pradesh, India during 2011–2013.Material and methodsDistrict-wise disease endemicity levels were mapped through geographical information system (GIS) tools. Spatial statistical analysis such as Getis-Ord Gi* was performed to identify hot spots and cold spots of dengue disease. Similarly self organizing maps (SOM), a datamining tool was also applied to understand the endemicity patterns in study areas.ResultsThe analysis shows that districts of Warangal, Karimnagar, Khammam and Vizianagaram are reported as hot spot regions whereas Adilabad and Nizamabad reported as cold spots for dengue. The SOM classify 23 districts in 03 major (07 sub) clusters. These SOM clusters were projected in the geographical space and based on the disease/cases intensity the districts were characterized into low, medium and high endemic areas.ConclusionThis visualization approach, SOM-GIS helps the public health officials to identify the disease endemic zones and take real time decisions for disease management.

Advanced and accurate forecasting of COVID‐19 cases plays a crucial role in planning and supplying resources effectively. Artificial Intelligence (AI) techniques have proved their capability in time series forecasting non‐linear problems. In the present study, the relationship between weather factor and COVID‐19 cases was assessed, and also developed a forecasting model using long short‐term memory (LSTM), a deep learning model. The study found that the specific humidity has a strong positive correlation, whereas there is a negative correlation with maximum temperature, and a positive correlation with minimum temperature was observed in various geographic locations of India. The weather data and COVID‐19 confirmed case data (1 April to 30 June 2020) were used to optimize univariate and multivariate LSTM time series forecast models. The optimized models were utilized to forecast the daily COVID‐19 cases for the period 1 July 2020 to 31 July 2020 with 1 to 14 days of lead time. The results showed that the univariate LSTM model was reasonably good for the short‐term (1 day lead) forecast of COVID‐19 cases (relative error <20%). Moreover, the multivariate LSTM model improved the medium‐range forecast skill (1–7 days lead) after including the weather factors. The study observed that the specific humidity played a crucial role in improving the forecast skill majorly in the West and northwest region of India. Similarly, the temperature played a significant role in model enhancement in the Southern and Eastern regions of India.

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Dengue is a rapidly spreading viral disease transmitted to humans by Aedes mosquitoes. Due to global urbanization and climate change, the number of dengue cases are gradually increasing in recent decades. Hence, an early prediction of dengue continues to be a major concern for public health in countries with high prevalence of dengue. Creating a robust forecast model for the accurate prediction of dengue is a complex task and can be done through various data modelling approaches. In the present study, we have applied vector auto regression, generalized boosted models, support vector regression, and long short-term memory (LSTM) to predict the dengue prevalence in Kerala state of the Indian subcontinent. We consider the number of dengue cases as the target variable and weather variables viz., relative humidity, soil moisture, mean temperature, precipitation, and NINO3.4 as independent variables. Various analytical models have been applied on both datasets and predicted the dengue cases. Among all the models, the LSTM model was outperformed with superior prediction capability (RMSE: 0.345 and R 2 :0.86) than the other models. However, other models are able to capture the trend of dengue cases but failed in predicting the outbreak periods when compared to LSTM. The findings of this study will be helpful for public health agencies and policymakers to draw appropriate control measures before the onset of dengue. The proposed LSTM model for dengue prediction can be followed by other states of India as well. Supplementary Information The online version contains supplementary material available at 10.1007/s00484-022-02405-z.

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