MedRN: Respiratory Tract Infections (Topic)最新文献

{"title":"Geospatial Analysis of the September 2020 Coronavirus Outbreak at the University of Wisconsin – Madison: Did a Cluster of Local Bars Play a Critical Role?","authors":"J. Harris","doi":"10.3386/w28132","DOIUrl":"https://doi.org/10.3386/w28132","url":null,"abstract":"We combined smartphone mobility data with census track-based reports of positive case counts to study a coronavirus outbreak at the University of Wisconsin-Madison campus, where nearly three thousand students had become infected by the end of September 2020. We identified a cluster of twenty bars located at the epicenter of the outbreak, in close proximity to on-campus residence halls and off-campus housing. Smartphones originating from the two hardest hit residence halls (Sellery and Witte), where about one in five students were infected, were 2.95 times more likely to visit the 20-bar cluster than smartphones originating in two more distant, less affected residence halls (Ogg and Smith). By contrast, smartphones from Sellery-Witte were only 1.55 times more likely than those from Ogg-Smith to visit a group of 68 restaurants in the same area. Physical proximity thus had a much stronger influence on bar visitation than on restaurant visitation (rate ratio 1.91, 95% CI 1.29-2.85, p = 0.0007). In a separate analysis, we determined the per-capita rates of visitation to the 20-bar cluster and to the 68-restaurant comparison group by smartphones originating in each of 19 census tracts in the university area, and related these visitation rates to the per-capita incidence of newly positive coronavirus tests in each census tract. In a multivariate regression, the visitation rate to the bar cluster was a significant determinant of infection rates (elasticity 0.90, 95% CI 0.26-1.54, p = 0.009), while the restaurant visitation rate showed no such relationship. Researchers and public health professionals need to think more about the potential super-spreader effects of clusters and networks of places, rather than individual sites.","PeriodicalId":132014,"journal":{"name":"MedRN: Respiratory Tract Infections (Topic)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134357817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-19 in Africa: Socio-economic Impact, Policy Response and Opportunities","authors":"Peterson K. Ozili","doi":"10.2139/ssrn.3574767","DOIUrl":"https://doi.org/10.2139/ssrn.3574767","url":null,"abstract":"PurposeThis paper examines the socio-economic impact of COVID-19 and the policy response in African countries.Design/methodology/approachThis study uses discourse analysis to analyse the socio-economic impact of COVID-19 in Africa.FindingsThe findings reveal that African countries have been affected by the coronavirus pandemic, and the effect was more severe for African regions compared to other regions. The rising pandemic affected social interaction and economic activities through the imposed social distancing policies that have different levels of strictness in several African countriesPractical implicationsThe implication of the findings is that social policies can affect the social and economic well-being of citizens. Secondly, the coronavirus outbreak has revealed how a biological crisis can be transformed to a sociological subject. The most important sociological consequence of the coronavirus outbreak for African citizens is the creation of social anxiety among families and households in the region. The outbreak has also shown how vulnerable African societies are in facing health hazards. Policymakers should enforce social policies that unite communities in bad times, to reduce social anxiety.Originality/valueThis is the first paper that explore the socio-economic impact of coronavirus and the policy response in African countries.","PeriodicalId":132014,"journal":{"name":"MedRN: Respiratory Tract Infections (Topic)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117225054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Further Estimations of the Likely Total Infections and Deaths Due to COVID19 in Select Countries (Version 2 dt. April 10, 2020)","authors":"Sebastian Morris","doi":"10.2139/ssrn.3574678","DOIUrl":"https://doi.org/10.2139/ssrn.3574678","url":null,"abstract":"We had earlier estimated the likely cases and deaths over the course of the pandemic for a number of countries. This was an early attempt and gave somewhat tentative results. With some 7 more days of data being now available, better estimates are possible which we bring out in this paper. As in the previous paper we use a logistic model of cumulative cases and deaths, to estimate the zero growth level of cases and deaths. We also provide an upper bound to these estimates. The earlier estimates are further reinforced, and new estimates are made for a select set of countries where the growth rates in the numbers of cases, and in deaths have begun to decline. We also give estimates of the current growth rates in cases and deaths that these countries are likely to witness. The study as before presumes that the spread of infection is one-stage logistic process, once significant numbers of infections have taken place. This may not be true of countries which witnessed low deaths and cases. In countries that have witnessed much spread and deaths relative to their populations and with more sustainable approaches to containment may not witness significantly more deaths than what has happened thus far. This would be the case of Iran, Italy. China and Korea too with their rather highly coordinated approach despite low spread of cases and low number of deaths relative to their population would along with Iran, Italy and Denmark and Turkey would most likely not see a secondary wave of infections. Argentina and South Africa show very high growth rate in deaths even the increase in cases have slowed down considerable. Spain has stabilized its growth in deaths to nearly zero levels bit since the cases are continuing to grow at around 5.7% the death rates could again turn positive after a while. Germany and Indonesia show continuing rise in deaths and cases at moderately high rates. Japan, Malaysia, Brazil and Singapore show low to moderate death rates, but since the rise in cases continues to be between 5 and 8%, these low(Japan) moderate growth rate in deaths are likely to continue for a while before they fall to zero. France, Sweden Australia and Thailand would see continuing growth in cases at moderate rates even though the growth in deaths continue to be at high rates. The US most notably shows very high growth rates in both deaths and in cases indicating that the deaths at high rates are likely to continue for a while. While estimates are made for Canada, India, Bangladesh, Russia, Mexico, UK and the Philippines, they are of limited value since it is too early for the logistic model to fit. However, all of these except Russia show high death rates and high case rates. These countries could all see continuing rise in cases before the decline in rates happen, so that their current decline in death rates even when statistically significant could change for the worse. We have as in the previous paper used a logistic model to estimate the current growth rates, an","PeriodicalId":132014,"journal":{"name":"MedRN: Respiratory Tract Infections (Topic)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130768179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Why Is COVID-19 Mortality in Lombardy so High? Evidence from the Simulation of a SEIHCR Model","authors":"Carlo A. Favero","doi":"10.2139/ssrn.3566865","DOIUrl":"https://doi.org/10.2139/ssrn.3566865","url":null,"abstract":"The standard SEIR model based on a parameterization consistent with the international evidence cannot explain the very high COVID-19 related mortality in Lombardy. This paper proposes an extension of the standard SEIR model that is capable of solving the puzzle. The SEIR model features exogenous mortality: once Susceptible individuals become first Exposed, and then Infected, they succumb with a given probability. The extended model inlcudes an Hospitlization process and the possibility that Hospitalized patients, who need to resort to Intensive Care Unit, cannot find availability because the ICU is saturated. This Constraint creates an additional increase in mortality, which is endogenous to the diffusion of the disease. The SEIHCR (H stands for Hospitalization and C stands for Constraint) is capable of explaining the dynamics of COVID-19 related mortality in Lombardy with a paramerization consistent with the international evidence.","PeriodicalId":132014,"journal":{"name":"MedRN: Respiratory Tract Infections (Topic)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114347687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-Pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality","authors":"Gandhi Krr, Murthy Kvr, Prasada Rao Ssp, F. Casella","doi":"10.2139/ssrn.3560688","DOIUrl":"https://doi.org/10.2139/ssrn.3560688","url":null,"abstract":"The momentum episode of novel coronavirus infection 2019 (COVID-19) represents a remarkable worldwide wellbeing and monetary risk to interconnected human social orders. Until an antibody is created, systems for controlling the flare-up depend on forceful social removing. In this connection, We tended to the significance of NPI’s based on R* value by grouping the nations. In light of R* values, we proposed the most ideal and NPI's for conceivable decrease of R* through CFR for the interest of all nations. In this manner, countries/regions can play it safe dependent on R* to diminish the spread of COVID-19.","PeriodicalId":132014,"journal":{"name":"MedRN: Respiratory Tract Infections (Topic)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122239242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}