ABSTRACT
According to the 2021 Behavioral Risk Factor Surveillance Survey, with an obesity prevalence rate of 37-2% in adults, Missouri continues to outpace the national average of 33-9%. The model described at https://wwwhealthiermo. org/-the Foundational Public Health Services (FPHS)-aims to assure six core public health programmatic areas are available through every public health agency: chronic disease prevention, communicable disease control, environmental public health,;injury prevention, maternal, child and family health, and linkages to medical, behavioral, and community resources. With funding from the Centers for Disease Control and Prevention (CDC), DHSS will lead an effort over the next four to five years to conduct a cost analysis for full implementation of the FPHS model statewide, as well as an accountability measure planning effort with a diverse group of stakeholders inclusive of county and state policymakers. DHSS Chief Medical Officer Heidi Miller, MD, MSMA member and an internal medicine physician with extensive experience with uninsured, Medicaid and underinsured populations, joined DHSS as the department's first Chief Medical Officer in January 2023- Dr. Miller will provide medical guidance and expertise to DHSS programs, serve as the liaison with medical associations and providers, be instrumental in program and protocol development, and continue to build and implement the vision of an integrated public health and healthcare system.
ABSTRACT
"Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)", the novel coronavirus, is responsible for the ongoing worldwide pandemic. "World Health Organization (WHO)" assigned an "International Classification of Diseases (ICD)" code-"COVID-19"-as the name of the new disease. Coronaviruses are generally transferred by people and many diverse species of animals, including birds and mammals such as cattle, camels, cats, and bats. Infrequently, the coronavirus can be transferred from animals to humans, and then propagate among people, such as with "Middle East Respiratory Syndrome (MERS-CoV)", "Severe Acute Respiratory Syndrome (SARS-CoV)", and now with this new virus, namely "SARS-CoV-2", or human coronavirus. Its rapid spreading has sent billions of people into lockdown as health services struggle to cope up. The COVID-19 outbreak comes along with an exponential growth of new infections, as well as a growing death count. A major goal to limit the further exponential spreading is to slow down the transmission rate, which is denoted by a "spread factor (f)", and we proposed an algorithm in this study for analyzing the same. This paper addresses the potential of data science to assess the risk factors correlated with COVID-19, after analyzing existing datasets available in "ourworldindata.org (Oxford University database)", and newly simulated datasets, following the analysis of different univariate "Long Short Term Memory (LSTM)" models for forecasting new cases and resulting deaths. The result shows that vanilla, stacked, and bidirectional LSTM models outperformed multilayer LSTM models. Besides, we discuss the findings related to the statistical analysis on simulated datasets. For correlation analysis, we included features, such as external temperature, rainfall, sunshine, population, infected cases, death, country, population, area, and population density of the past three months - January, February, and March in 2020. For univariate timeseries forecasting using LSTM, we used datasets from 1 January 2020, to 22 April 2020.