Browsing by Subject "COVID-19 / epidemiology"
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Item A Computational Modeling Study of COVID-19 in Bangladesh(The American Society of Tropical Medicine and Hygiene, 2020-11-02) Khan, Irtesam Mahmud; Haque, Ubydul; Kaisar, Samiha; Rahman, Mohammad SohelThe COVID-19 pandemic has spread globally. Only three cases in Bangladesh were reported on March 8, 2020. Here, we aim to predict the epidemic progression for 1 year under different scenarios in Bangladesh. We extracted the number of daily confirmed cases from March 8 to July 20, 2020. We considered the suspected-infected-removed (SIR) model and performed a maximum likelihood-based grid search to determine the removal rate (). The transmission was modeled as a stochastic random walk process, and sequential Monte Carlo simulation was run 100 times with bootstrap fits to infer the transmission rate (beta) and R t. According to the simulation, the (real) peak daily incidence of 3,600 would be followed by a steady decline, reaching below 1,000 in late January 2021. Thus, the model predicted that there would still be more than 300 cases/day even after a year. However, with proper interventions, a much steeper decline would be achieved following the peak. If we apply a combined (0.8beta, 1.2) intervention, there would be less than 100 cases by mid-October, only around five odd cases at the beginning of the year 2021, and zero cases in early March 2021. The predicted total number of deaths (in status quo) after 1 year would be 8,533 which would reduce to 3,577 if combined (0.8beta, 1.2) intervention is applied. We have also predicted the ideal number of tests that Bangladesh should perform and based on that redid the whole simulation. The outcome, though worse, would be manageable with interventions according to the simulation.Item An epidemic model for non-first-order transmission kinetics(PLOS, 2021-03-11) Mun, Eun-Young; Geng, FengCompartmental models in epidemiology characterize the spread of an infectious disease by formulating ordinary differential equations to quantify the rate of disease progression through subpopulations defined by the Susceptible-Infectious-Removed (SIR) scheme. The classic rate law central to the SIR compartmental models assumes that the rate of transmission is first order regarding the infectious agent. The current study demonstrates that this assumption does not always hold and provides a theoretical rationale for a more general rate law, inspired by mixed-order chemical reaction kinetics, leading to a modified mathematical model for non-first-order kinetics. Using observed data from 127 countries during the initial phase of the COVID-19 pandemic, we demonstrated that the modified epidemic model is more realistic than the classic, first-order-kinetics based model. We discuss two coefficients associated with the modified epidemic model: transmission rate constant k and transmission reaction order n. While k finds utility in evaluating the effectiveness of control measures due to its responsiveness to external factors, n is more closely related to the intrinsic properties of the epidemic agent, including reproductive ability. The rate law for the modified compartmental SIR model is generally applicable to mixed-kinetics disease transmission with heterogeneous transmission mechanisms. By analyzing early-stage epidemic data, this modified epidemic model may be instrumental in providing timely insight into a new epidemic and developing control measures at the beginning of an outbreak.Item Leading Predictors of COVID-19-Related Poor Mental Health in Adult Asian Indians: An Application of Extreme Gradient Boosting and Shapley Additive Explanations(MDPI, 2023-01-09) Ikram, Mohammad; Shaikh, Nazneen F.; Vishwanatha, Jamboor K.; Sambamoorthi, UshaDuring the COVID-19 pandemic, an increase in poor mental health among Asian Indians was observed in the United States. However, the leading predictors of poor mental health during the COVID-19 pandemic in Asian Indians remained unknown. A cross-sectional online survey was administered to self-identified Asian Indians aged 18 and older (N = 289). Survey collected information on demographic and socio-economic characteristics and the COVID-19 burden. Two novel machine learning techniques-eXtreme Gradient Boosting and Shapley Additive exPlanations (SHAP) were used to identify the leading predictors and explain their associations with poor mental health. A majority of the study participants were female (65.1%), below 50 years of age (73.3%), and had income >/= $75,000 (81.0%). The six leading predictors of poor mental health among Asian Indians were sleep disturbance, age, general health, income, wearing a mask, and self-reported discrimination. SHAP plots indicated that higher age, wearing a mask, and maintaining social distancing all the time were negatively associated with poor mental health while having sleep disturbance and imputed income levels were positively associated with poor mental health. The model performance metrics indicated high accuracy (0.77), precision (0.78), F1 score (0.77), recall (0.77), and AUROC (0.87). Nearly one in two adults reported poor mental health, and one in five reported sleep disturbance. Findings from our study suggest a paradoxical relationship between income and poor mental health; further studies are needed to confirm our study findings. Sleep disturbance and perceived discrimination can be targeted through tailored intervention to reduce the risk of poor mental health in Asian Indians.Item The human toll and humanitarian crisis of the Russia-Ukraine war: the first 162 days(BMJ Publishing Group Ltd., 2022-09-28) Haque, Ubydul; Naeem, Amna; Wang, Shanshan; Espinoza, Juan; Holovanova, Irina; Gutor, Taras; Bazyka, Dimitry; Galindo, Rebeca; Sharma, Sadikshya; Kaidashev, Igor P.; Chumachenko, Dmytro; Linnikov, Syvatoslav; Annan, Esther; Lubinda, Jailos; Korol, Natalya; Bazyka, Kostyantyn; Zhyvotovska, Liliia; Zimenkovsky, Andriy; Nguyen, Uyen-Sa D.T.BACKGROUND: We examined the human toll and subsequent humanitarian crisis resulting from the Russian invasion of Ukraine, which began on 24 February 2022. METHOD: We extracted and analysed data resulting from Russian military attacks on Ukrainians between 24 February and 4 August 2022. The data tracked direct deaths and injuries, damage to healthcare infrastructure and the impact on health, the destruction of residences, infrastructure, communication systems, and utility services - all of which disrupted the lives of Ukrainians. RESULTS: As of 4 August 2022, 5552 civilians were killed outright and 8513 injured in Ukraine as a result of Russian attacks. Local officials estimate as many as 24 328 people were also killed in mass atrocities, with Mariupol being the largest (n=22 000) such example. Aside from wide swaths of homes, schools, roads, and bridges destroyed, hospitals and health facilities from 21 cities across Ukraine came under attack. The disruption to water, gas, electricity, and internet services also extended to affect supplies of medications and other supplies owing to destroyed facilities or production that ceased due to the war. The data also show that Ukraine saw an increase in cases of HIV/AIDS, tuberculosis, and Coronavirus (COVID-19). CONCLUSIONS: The 2022 Russia-Ukraine War not only resulted in deaths and injuries but also impacted the lives and safety of Ukrainians through destruction of healthcare facilities and disrupted delivery of healthcare and supplies. The war is an ongoing humanitarian crisis given the continuing destruction of infrastructure and services that directly impact the well-being of human lives. The devastation, trauma and human cost of war will impact generations of Ukrainians to come.