Instead of traditional statistical models for large spatial areas and weekly or monthly temporal units, what public health workers urgently need is a timely risk prediction method for small areas. This risk prediction would provide information for early warning, target surveillance, and intervention. Daily dengue cases in the 25 Turbat city, there were in total 205 confirmed dengue cases during this period. A logistic regression model was fitted to the daily incidents occurring in the city for the past 30 days. The fitted model was then used to predict the incidence probabilities of dengue outbreaks for the city the next day. Fitted incidence probabilities were chosen to determine a cut-point for issuing the alerts.

            The covariates included three different levels of spatial effect, and four lag time periods. The population density and the meteorological conditions were also included in the prediction. The performance of the prediction models was evaluated on 122 consecutive days from march1 to April 20, 2022. With the 80th percentile threshold, median the median sensitivity was 8% and the median false positive rate was 2%. We found that most of the coefficients of the predictors of having cases in the same city in the previous 14 days were positive and significant for the 48 daily updated models. The estimated coefficients of population density were significant during the peak of the epidemic in 2022. The proposed method can provide near real-time dengue risk prediction for a small area.

            This can serve as a useful decision-making tool for front-line public health workers to control dengue epidemics. The precision of the spatial and temporal units can be easily adjusted to different settings for different cities.



By: Essa Sayad

Koshk Malikabad