Since its discovery in the Hubei province of China, the global spread of the novel coronavirus SARS-CoV-2 has resulted in millions of COVID-19 cases and hundreds of thousands of deaths. The spread throughout Asia, Europe, and the Americas has presented one of the greatest infectious disease threats in recent history and has tested the capacity of global health infrastructures. Since no effective vaccine is available, isolation techniques to prevent infection such as home quarantine and social distancing while in public have remained the cornerstone of public health interventions. While government and health officials were charged with implementing stay-at-home strategies, many of which had little guidance as to the consequences of how quickly to begin them. Moreover, as the local epidemic curves have been flattened, the same officials must wrestle with when to ease or cease such restrictions as to not impose economic turmoil. To evaluate the effects of quarantine strategies during the initial epidemic, an agent based modeling framework was created to take into account local spread based on geographic and population data with a corresponding interactive desktop and web-based application. Using the state of Massachusetts in the United States of America, we have illustrated the consequences of implementing quarantines at different time points after the initial seeding of the state with COVID-19 cases. Furthermore, we suggest that this application can be adapted to other states, small countries, or regions within a country to provide decision makers with critical information necessary to best protect human health.

Since its discovery in the Hubei province of China, the global spread of the novel coronavirus SARS-CoV-2 has resulted in millions of COVID-19 cases and hundreds of thousands of deaths. The spread throughout Asia, Europe, and the Americas has presented one of the greatest infectious disease threats in recent history and has tested the capacity of global health infrastructures. Since no effective vaccine is available, isolation techniques to prevent infection such as home quarantine and social distancing while in public have remained the cornerstone of public health interventions. While government and health officials were charged with implementing stay-at-home strategies, many of which had little guidance as to the consequences of how quickly to begin them. Moreover, as the local epidemic curves have been flattened, the same officials must wrestle with when to ease or cease such restrictions as to not impose economic turmoil. To evaluate the effects of quarantine strategies during the initial epidemic, an agent based modeling framework was created to take into account local spread based on geographic and population data with a corresponding interactive desktop and web-based application. Using the state of Massachusetts in the United States of America, we have illustrated the consequences of implementing quarantines at different time points after the initial seeding of the state with COVID-19 cases. Furthermore, we suggest that this application can be adapted to other states, small countries, or regions within a country to provide decision makers with critical information necessary to best protect human health.

The novel coronavirus SARS-CoV-2 was first detected in China in December 2019 and has rapidly spread around the globe. The World Health Organization declared COVID-19 a pandemic in March 2020 just three months after the introduction of the virus. Individual nations have implemented and enforced a variety of social distancing interventions to slow the virus spread, that had different degrees of success. Understanding the role of non-pharmaceutical interventions (NPIs) on COVID-19 transmission in different settings is highly important. While most such studies have focused on China, neighboring Asian counties, Western Europe, and North America, there is a scarcity of studies for Eastern Europe. The aim of this epidemiological study is to fill this gap by analyzing the characteristics of the first months of the epidemic in Ukraine using agent-based modelling and phylodynamics. Specifically, first we studied the dynamics of COVID-19 incidence and mortality and explored the impact of epidemic NPIs. Our stochastic model suggests, that even a small delay of weeks could have increased the number of cases by up to 50%, with the potential to overwhelm hospital systems. Second, the genomic data analysis suggests that there have been multiple introductions of SARS-CoV-2 into Ukraine during the early stages of the epidemic. Our findings support the conclusion that the implemented travel restrictions may have had limited impact on the epidemic spread. Third, the basic reproduction number for the epidemic that has been estimated independently from case counts data and from genomic data suggest sustained intra-country transmissions.

The novel coronavirus SARS-CoV-2 emerged in China in December 2019 and has rapidly spread around the globe. The World Health Organization declared COVID-19 a pandemic in March 2020 just three months after the introduction of the virus. Individual nations have implemented and enforced with varying degrees of success a variety of social distancing interventions to slow the virus spread. Investigating the role of non-pharmaceutical interventions on COVID-19 transmission in different settings is an important research. While most transmission modeling studies have focused on the dynamics in China, neighboring Asian counties, Western Europe, and North America, there is a scarcity of studies for Eastern Europe. This study starts to fill this gap by analyzing the characteristics of the first epidemic wave in Ukraine using mathematical and statistical models together with epidemiological and genomic sequencing data. Using an agent-based model, the trajectory of the first wave in terms of cases and deaths and explore the impact of quarantine strategies via simulation studies have been characterized. The implemented stochastic model for epidemic counts suggests, that even a small delay of weeks could have increased the number of cases by up to 50\%, with the potential to overwhelm hospital systems. The genomic data analysis suggests that there have been multiple introductions of SARS-CoV-2 into Ukraine during the early stages of the epidemic with eight distinct transmission clusters identified. The basic reproduction number for the epidemic has been estimated independently both from case counts data and from genomic data. The findings support the hypothesis that, the public health measures did not have a decreasing effect on the existing viral population number at the time of implementation, since strains were detected after the quarantine date. However, the public health measures did help to prevent the appearance of new (and potentially more virulent) SARS-CoV-2 variants in Ukraine.

With the novel coronavirus 19 (COVID-19) continually having a devastating effect around the globe, many scientists and clinicians are actively seeking to develop new techniques to assist with the tackling of this disease. Modern machine learning methods have shown promise in their adoption to assist the health care industry through their data and analytics-driven decision making, inspiring researchers to develop new angles to fight the virus. In this paper, we aim to develop a robust method for the detection of COVID-19 by utilizing patients' chest X-ray images. Despite recent progress, scarcity of data has thus far limited the development of a robust solution. We extend upon existing work by combing publicly available data across 5 different sources and carefully annotating the comprising images into three categories: normal, pneumonia, and COVID-19. To achieve a high classification accuracy, we propose a training pipeline based on the directed guidance of traditional classification networks, where the guidance is directed by an external segmentation network. Through this network, we observed that the widely used, standard networks can achieve an accuracy comparable to tailor-made models specifically for COVID-19, furthermore one network, VGG-16, outperformed the best of the tailor-made models.