An Association between Rainy Days with Clinical Dengue Fever in Dhaka, Bangladesh: Findings from a Hospital Based Study

Abstract

Background: Dengue, a febrile illness, is caused by a Flavivirus transmitted by Aedes aegypti and Aedes albopictus mosquitoes. Climate influences the ecology of the vectors. We aimed to identify the influence of climatic variability on the occurrence of clinical dengue requiring hospitalization in Zone-5, a high incidence area of Dhaka City Corporation (DCC), Bangladesh. Methods and Findings: We retrospectively identified clinical dengue cases hospitalized from Zone-5 of DCC between 2005 and 2009. We extracted records of the four major catchment hospitals of the study area. The Bangladesh Meteorological Department (BMD) provided data on temperature, rainfall, and humidity of DCC for the study period. We used autoregressive integrated moving average (ARIMA) models for the number of monthly dengue hospitalizations. We also modeled all the climatic variables using Poisson regression. During our study period, dengue occurred throughout the year in Zone-5 of DCC. The median number of hospitalized dengue cases was 9 per month. Dengue incidence increased sharply from June, and reached its peak in August. One additional rainy day per month increased dengue cases in the succeeding month by 6% (RR = 1.06, 95% CI: 1.04–1.09). Conclusions: Dengue is transmitted throughout the year in Zone-5 of DCC, with seasonal variation in incidence. The number of rainy days per month is significantly associated with dengue incidence in the subsequent month. Our study suggests the initiation of campaigns in DCC for controlling dengue and other Aedes mosquito borne diseases, including Chikunguniya from the month of May each year. BMD rainfall data may be used to determine campaign timing.

A community based case-control study to determine the risk factors of dengue fever in Bangladesh

“…The travel history of the patient was revealed as a significant risk factor for the dengue infection in this study as described by others (Verma et al 2014). A portion of our cases (n=57) visited the capital Dhaka which was the hotspot of outbreaks and circulate infection throughout the year (Rahman et al 2020). A study in India found that 70% of cases traveled to neighboring cities and expressed the signs of infection within the following 7 days (Swain et al 2020).…”

A case-control study to determine the risk factors of dengue fever in Chattogram, Bangladesh

“…The patient's travel history was revealed as a significant risk factor for the dengue infection in this study as described by others [ 23 ]. A portion of our cases (n = 57) visited the capital Dhaka which was the hotspot of outbreaks and circulated infection throughout the year [ 24 ]. A study in India found that 70% of cases traveled to neighboring cities and expressed the signs of infection within the following 7 days [ 14 ].…”

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

“…When we evaluated the studies regarding the types of models used in the predictions, we observed that the vast majority of authors investigated moving average models (27), such as the Autoregressive Integrated Moving Average (ARIMA) (17,23,29,35,41,43,46,56,(61)(62)(63), Seasonal Autoregressive Integrated Moving Average (SARIMA) (55,(63)(64)(65)(66), Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) (67). Several works have also presented a wide variety of models using artificial neural networks, mainly the LSTM (59,(68)(69)(70).…”