Clinico-epidemiological Characteristics of Asymptomatic and Symptomatic COVID-19-Positive Patients in Bangladesh.

As of August 15, 2020, Bangladesh lost 3591 lives since the first Coronavirus disease 2019 (COVID-19) case announced on March 8. The objective of the study was to report the clinical manifestation of both symptomatic and asymptomatic COVID-19-positive patients. An online-based cross-sectional survey was conducted for initial recruitment of participants with subsequent telephone interview by the three trained physicians in 237 adults with confirmed COVID-19 infection in Bangladesh. The study period was 27 April to 26th May 2020. Consent was ensured before commencing the interview. Collected data were entered in a pre-designed case record form and subsequently analyzed by SPSS 20.0. The mean±SD age at presentation was 41.59±13.73 years and most of the cases were male (73.0%). A total of 90.29% of patients reside in urban areas. Among the positive cases, 13.1% (n=31) were asymptomatic. Asymptomatic cases were significantly more common in households with 2 to 4 members (p=0.008). Both symptomatic and asymptomatic patients shared similar ages of presentation (p=0.23), gender differences (p=0.30) and co-morbidities (p=0.11). Only 5.3% of patients received ICU care during their treatment. The most frequent presentation was fever (88.3%), followed by cough (69.9%), chest pain (34.5%), body ache (31.1%), and sore throat (30.1%). Thirty-nine percent (n=92) of the patients had co-morbidities, with diabetes and hypertension being the most frequently observed. There has been an upsurge in COVID-19 cases in Bangladesh. Patients were mostly middle-aged and male. Typical presentations were fever and cough. Maintenance of social distancing and increased testing are required to meet the current public health challenge.

A Fuzzy Time Series Prediction Model of the COVID-19 Epidemic

In our study, a number of time series analysis techniques are applied to the COVID-19 epidemic data. The result of the analysis is used to develop three single variable fuzzy time series models from the dataset of day to day newly confirmed cases of the virus in certain countries. Those developed models are later applied on infected cases of the countries that are not used before in model development. This helps in appropriate model selection for prediction of cases for unseen countries. The forecasted results are then compared and analyzed in detail with different performance metric. The time series analysis process described in this article can be used to better understand the epidemic trend of a particular country and help the government plan better intervention policies. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.