Browsing by Author "Haque, Ubydul"

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Impact of environmental indicators on the COVID-19 pandemic in Delhi, India
(MDPI, 2021-08-09T00:00:00) Mangla, Sherry; Pathak, Ashok Kumar; Arshad, Mohd.; Ghosh, Doyel; Sahoo, Prafulla Kumar; Garg, Vinod Kumar; Haque, Ubydul
Currently, there is a massive debate on whether meteorological and air quality parameters play a crucial role in the transmission of COVID-19 across the globe. With this background, this study aims to evaluate the impact of air pollutants (PM2.5, PM10, CO, NO, NO2, and O3) and meteorological parameters (temperature, humidity, wind speed, and rainfall) on the spread and mortality due to the COVID-19 outbreak in Delhi from 14 Mar 2020 to 3 May 2021. The Spearman�s rank correlation method employed on secondary data shows a significant correlation between the COVID-19 incidences and the PM2.5, PM10, CO, NO, NO2, and O3 concentrations. Amongst the four meteorological parameters, temperature is strongly correlated with COVID-19 infections and deaths during the three phases, i.e., pre-lockdown (14 March 2020 to 24 March 2020) (r = 0.79), lockdown (25 March 2020 to 31 May 2020) (r = 0.87), and unlock (1 June 2020 to 3 May 2021) (r = ?0.75), explaining the variability of about 20�30% in the lockdown period and 18�19% in the unlock period. NO2 explained the maximum variability of 10% and 7% in the total confirmed cases and deaths among the air pollutants, respectively. A generalized linear model could explain 80% and 71% of the variability in confirmed cases and deaths during the lockdown and 82% and 81% variability in the unlock phase, respectively. These findings suggest that these factors may contribute to the transmission of the COVID-19 and its associated deaths. The study results would enhance the ongoing research related to the influence of environmental factors. They would be helpful for policymakers in managing the outbreak of COVID-19 in Delhi, India. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Moderation effects of serotype on dengue severity across pregnancy status in Mexico
(BioMed Central Ltd, 2023-03-26T00:00:00) Annan, Esther; Nguyen, Uyen-Sa D. T.; Trevi�o, Jes�s; Wan Yaacob, Wan Fairos; Mangla, Sherry; Pathak, Ashok Kumar; Nandy, Rajesh; Haque, Ubydul
Background: Pregnancy increases a woman�s risk of severe dengue. To the best of our knowledge, the moderation effect of the dengue serotype among pregnant women has not been studied in Mexico. This study explores how pregnancy interacted with the dengue serotype from 2012 to 2020 in Mexico. Method: Information from 2469 notifying health units in Mexican municipalities was used for this cross-sectional analysis. Multiple logistic regression with interaction effects was chosen as the final model and sensitivity analysis was done to assess potential exposure misclassification of pregnancy status. Results: Pregnant women were found to have higher odds of severe dengue [1.50 (95% CI 1.41, 1.59)]. The odds of dengue severity varied for pregnant women with DENV-1 [1.45, (95% CI 1.21, 1.74)], DENV-2 [1.33, (95% CI 1.18, 1.53)] and DENV-4 [3.78, (95% CI 1.14, 12.59)]. While the odds of severe dengue were generally higher for pregnant women compared with non-pregnant women with DENV-1 and DENV-2, the odds of disease severity were much higher for those infected with the DENV-4 serotype. Conclusion: The effect of pregnancy on severe dengue is moderated by the dengue serotype. Future studies on genetic diversification may potentially elucidate this serotype-specific effect among pregnant women in Mexico. � 2023, The Author(s).
Short-term forecasting of the COVID-19 outbreak in India
(Oxford University Press, 2021-05-28T00:00:00) Mangla, Sherry; Pathak, Ashok Kumar; Arshad, Mohd; Haque, Ubydul
As the outbreak of coronavirus disease 2019 (COVID-19) is rapidly spreading in different parts of India, a reliable forecast for the cumulative confirmed cases and the number of deaths can be helpful for policymakers in making the decisions for utilizing available resources in the country. Recently, various mathematical models have been used to predict the outbreak of COVID-19 worldwide and also in India. In this article we use exponential, logistic, Gompertz growth and autoregressive integrated moving average (ARIMA) models to predict the spread of COVID-19 in India after the announcement of various unlock phases. The mean absolute percentage error and root mean square error comparative measures were used to check the goodness-of-fit of the growth models and Akaike information criterion for ARIMA model selection. Using COVID-19 pandemic data up to 20 December 2020 from India and its five most affected states (Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu and Kerala), we report 15-days-ahead forecasts for cumulative confirmed cases and the number of deaths. Based on available data, we found that the ARIMA model is the best-fitting model for COVID-19 cases in India and its most affected states. � 2021 The Author(s) 2021.