scholarly journals Estimation of the fraction of COVID-19 infected people in U.S. states and countries worldwide

2020 ◽  
Author(s):  
Jungsik Noh ◽  
Gaudenz Danuser
Keyword(s):  
New York ◽  
Health Policies ◽  
Test Results ◽  
Virus Spread ◽  
Public Health Policies ◽  
Infected People ◽  
Learning Framework ◽  
Time Courses ◽  
Definition Of ◽  
The U.S

Since the beginning of the COVID-19 pandemic, daily counts of confirmed cases and deaths have been publicly reported in real-time to control the virus spread. However, substantial undocumented infections have obscured the true prevalence of the virus. A machine learning framework was developed to estimate time courses of actual new COVID-19 cases and current infections in 50 countries and 50 U.S. states from reported test results and deaths, as well as published epidemiological parameters. Severe under-reporting of cases was found to be universal. Our framework projects for countries like Belgium, Brazil, and the U.S. ~10% of the population has been once infected. In the U.S. states like Louisiana, Georgia, and Florida, more than 4% of the population is estimated to be currently infected, as of September 3, 2020, while in New York the fraction is 0.12%. The estimation of the actual fraction of currently infected people is crucial for any definition of public health policies, which up to this point may have been misguided by the reliance on confirmed cases.

scholarly journals Estimation of the fraction of COVID-19 infected people in U.S. states and countries worldwide

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246772 ◽  
Author(s):  
Jungsik Noh ◽  
Gaudenz Danuser
Keyword(s):  
Public Health ◽  
New York ◽  
Time Course ◽  
Test Results ◽  
Public Health Policies ◽  
Infected People ◽  
Learning Framework ◽  
Time Courses ◽  
Definition Of ◽  
The U.S

Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, daily counts of confirmed cases and deaths have been publicly reported in real-time to control the virus spread. However, substantial undocumented infections have obscured the true size of the currently infected population, which is arguably the most critical number for public health policy decisions. We developed a machine learning framework to estimate time courses of actual new COVID-19 cases and current infections in all 50 U.S. states and the 50 most infected countries from reported test results and deaths. Using published epidemiological parameters, our algorithm optimized slowly varying daily ascertainment rates and a time course of currently infected cases each day. Severe under-ascertainment of COVID-19 cases was found to be universal across U.S. states and countries worldwide. In 25 out of the 50 countries, actual cumulative cases were estimated to be 5–20 times greater than the confirmed cases. Our estimates of cumulative incidence were in line with the existing seroprevalence rates in 46 U.S. states. Our framework projected for countries like Belgium, Brazil, and the U.S. that ~10% of the population has been infected once. In the U.S. states like Louisiana, Georgia, and Florida, more than 4% of the population was estimated to be currently infected, as of September 3, 2020, while in New York this fraction is 0.12%. The estimation of the actual fraction of currently infected people is crucial for any definition of public health policies, which up to this point may have been misguided by the reliance on confirmed cases.

scholarly journals A Bayesian Framework for Estimating the Risk Ratio of Hospitalization for People with Comorbidity Infected by the SARS-CoV-2 Virus

2020 ◽  
Author(s):  
Xiang Gao ◽  
Qunfeng Dong
Keyword(s):  
Public Health ◽  
Cardiovascular Diseases ◽  
General Population ◽  
Risk Stratification ◽  
Large Scale ◽  
Chronic Respiratory Disease ◽  
Health Policies ◽  
Public Health Policies ◽  
Infected People ◽  
The U.S

Estimating the hospitalization risk for people with certain comorbidities infected by the SARS-CoV-2 virus is important for developing public health policies and guidance based on risk stratification. Traditional biostatistical methods require knowing both the number of infected people who were hospitalized and the number of infected people who were not hospitalized. However, the latter may be undercounted, as it is limited to only those who were tested for viral infection. In addition, comorbidity information for people not hospitalized may not always be readily available for traditional biostatistical analyses. To overcome these limitations, we developed a Bayesian approach that only requires the observed frequency of comorbidities in COVID-19 patients in hospitals and the prevalence of comorbidities in the general population. By applying our approach to two different large-scale datasets in the U.S., our results consistently indicated that cardiovascular diseases carried the highest hospitalization risk for COVID-19 patients, followed by diabetes, chronic respiratory disease, hypertension, and obesity, respectively.

scholarly journals Experience report on strategy design for effective public health policies in the Piauí, Brazil, 2020

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
L Vilarinho ◽  
A Amorim ◽  
C Fé ◽  
O Cardoso
Keyword(s):  
Public Health ◽  
Life Expectancy ◽  
The State ◽  
Health Policies ◽  
State Planning ◽  
Public Health Policies ◽  
Strategic Actions ◽  
Health Area ◽  
Swot Matrix ◽  
Definition Of

Abstract Aim Report of the ascending, collective and participative process of construction of the Strategic Action Plan of the State Secretariat of Health of Piauí for the period from 2020 to 2023 and its compatibility with the Planning and Management Instrument of the State of Piauí - PPA (Plano Pluri Yearly). Action developed for the Institutional Development Program of SUS. We sought consistency and compatibility for health needs and Government priorities, implementing public health policies, technical, operational and financial feasibility and feasibility, impact and improvement in living conditions and health of the population, reducing inequalities, expanding the access to inclusive health policies, increase in citizen's life expectancy and life expectancy at birth. Methods Workshops, with technical staff from the Health Secretariat and the State Planning Secretariat, using the Situational Strategic Planning-PES, in addition to the Balanced Scorecard, and the SWOT Matrix. The SESAPI Strategic Map was previously built, with priorities for the identification of plans and results oriented to the goals. Results The SWOT Matrix focused on analyzing the environment or scenarios, internal and external, with strategies to maintain strengths, reduce the intensity of weaknesses, use opportunities and protect against threats. The technical health priorities were legitimized and made compatible with the State Planning Secretariat, in Workshops with Social Representations of the 12 Regional Development Territories of the State. Conclusions The prioritized strategic actions embodied the definition of Budgetary Actions for the health area that conform to the LOA- Annual Budget Law, linking the estimated amounts to a set of indicators and desired and possible results to be achieved in the established period. The entire process was technically monitored and submitted to analysis and approval by the Social Control bodies in compliance with the provisions of the legislation in force. Key messages The prioritized strategic actions embodied the definition of Budgetary Actions for the health area and linked the estimated amounts to a set of indicators. All production has generated the formation of a government staff with managerial capacity to strategic planning and evaluation as part of training teams trained by the government.

scholarly journals Impact of a new SARS-CoV-2 variant on the population: A mathematical modeling approach

2021 ◽  
Author(s):  
Gilberto Gonzalez-Parra ◽  
David Martínez-Rodríguez ◽  
Rafael-J. Villanueva-Micó
Keyword(s):  
Death Rate ◽  
Health Policies ◽  
Public Health Policies ◽  
Infected People ◽  
Scientific Support ◽  
New Variant ◽  
Health Institutions ◽  
Different Characteristics ◽  
The Impact ◽  
Pharmaceutical Interventions

Several SARS-CoV-2 variants have emerged around the world and the appearance of other variants depends on many factors. These new variants might have different characteristics that can affect the transmissibility and death rate. The administration of vaccines against the coronavirus disease 2019 (COVID-19) started in early December of 2020 and in some countries the vaccines will not soon be widely available. In this article, we study the impact of a new more transmissible SARS-CoV-2 strain on prevalence, hospitalizations, and deaths related to the SARS-CoV-2 virus. We study different scenarios regarding the transmissibility in order to provide a scientific support for public health policies and bring awareness of potential future situations related to the COVID-19 pandemic. We construct a compartmental mathematical model based on differential equations to study these different scenarios. In this way, we are able to understand how a new, more infectious strain of the virus can impact the dynamics of the COVID-19 pandemic. We study several metrics related to the possible outcomes of the COVID-19 pandemic in order to assess the impact of a higher transmissibility of a new SARS-CoV-2 strain on these metrics. We found that, even if the new variant has the same death rate, its high transmissibility can increase the number of infected people, those hospitalized, and deaths. The simulation results show that health institutions need to focus on increasing non-pharmaceutical interventions and the pace of vaccine inoculation since a new variant with higher transmissibility as, for example, VOC-202012/01 of lineage B.1.1.7, may cause more devastating outcomes in the population.

scholarly journals Impact of Vaccination on the COVID-19 Pandemic: Evidence from U.S. States

2021 ◽  
Author(s):  
Xiao Chen ◽  
Hanwei Huang ◽  
Jiandong Ju ◽  
Ruoyan Sun ◽  
Jialiang Zhang
Keyword(s):  
Herd Immunity ◽  
Sir Model ◽  
Health Policies ◽  
Vaccine Hesitancy ◽  
The Novel ◽  
Vaccination Programs ◽  
Public Health Policies ◽  
Percentage Points ◽  
Novel Coronavirus ◽  
The U.S

Abstract Governments worldwide are implementing mass vaccination programs in an effort to end the novel coronavirus (COVID-19) pandemic. Here, we evaluated the effectiveness of the COVID-19 vaccination program and predicted the path to herd immunity in the U.S. We estimated that vaccination reduced the total number of new cases by 4.4 million (from 33.0 to 28.6 million), prevented approximately 0.12 million hospitalizations (from 0.89 to 0.78 million), and decreased the population infection rate by 1.34 percentage points (from 10.10–8.76%). We built a Susceptible-Infected-Recovered (SIR) model with vaccination to predict herd immunity. Our model predicts that the U.S. can achieve herd immunity by the last week of July 2021, with a cumulative vaccination coverage of 60.2%. Herd immunity could be achieved earlier with a faster vaccination pace, lower vaccine hesitancy, and higher vaccine effectiveness. These findings improve our understanding of the COVID-19 vaccination and can inform future public health policies.

scholarly journals Definition of Personalized Medicine and Targeted Therapies: Does Medical Familiarity Matter?

2021 ◽  
Vol 11 (1) ◽  
pp. 26
Author(s):  
Valentyn Fournier ◽  
Thomas Prebet ◽  
Alexandra Dormal ◽  
Maïté Brunel ◽  
Robin Cremer ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Targeted Therapies ◽  
Scientific Literature ◽  
Emotional Valence ◽  
Health Policies ◽  
Medical Field ◽  
Patient Centered ◽  
Public Health Policies ◽  
Academic Fields ◽  
Definition Of

Personalized medicine (PM) is increasingly becoming a topic of discussion in public health policies and media. However, there is no consensus among definitions of PM in the scientific literature and the terms used to designate it, with some definitions emphasizing patient-centered aspects and others emphasizing biomedical aspects. Furthermore, terms used to refer to PM (e.g., “pharmacogenomics” or, more often, “targeted therapies”) are diverse and differently used. To our knowledge, no study has apprehended the differences of definition and attitudes toward personalized medicine and targeted therapies according to level of familiarity with the medical field. Our cohort included 349 French students from three different academic fields, which modulated their familiarity level with the medical field. They were asked to associate words either to “personalized medicine” or “target therapies”. Then, they were asked to give an emotional valence to their associations. Results showed that nonfamiliar students perceived PM as more positive than targeted therapies (TT), whereas familiar students showed no difference. Only familiar students defined PM and TT with technical aspects such as genetics or immunology. Further studies are needed in the field in order to determine which other factors could influence the definitions of PM and TT and determine how these definitions could have an impact in a clinical setting.

scholarly journals Revealing the influence of national public health policies for the outbreak of the SARS-CoV-2 epidemic in Wuhan, China through status dynamic modeling

2020 ◽  
Author(s):  
Tianyi Qiu ◽  
Han Xiao
Keyword(s):  
Public Health ◽  
Real Time ◽  
Dynamic Model ◽  
Health Policies ◽  
Chinese Government ◽  
Wuhan City ◽  
Public Health Policies ◽  
Infected People ◽  
End Point ◽  
National Public Health

SummaryBackgroundThe epidemic caused by SARS-CoV-2 was first reported in Wuhan, China, and now is spreading worldwide. The Chinese government responded to this epidemic with multiple public health policies including locking down the city of Wuhan, establishing multiple temporary hospitals, and prohibiting public gathering events. Here, we constructed a new real-time status dynamic model of SEIO (MH) to reveal the influence of national public health policies and to model the epidemic in Wuhan.MethodsA real-time status dynamic model was proposed to model the population of Wuhan in status Susceptible (S), Exposed (E), Infected with symptoms (I), with Medical care (M), and Out of the system (O) daily. Model parameters were fitted according to the daily report of new infections from Jan. 27th, 2020 to Feb. 2nd, 2020. Using the fitted parameters, the epidemic under different conditions was simulated and compared with the current situation.FindingAccording to our study, the first patient is most likely appeared on Nov. 29th, 2019. There had already been 4,153 infected people and 6,536 exposed ones with the basic reproduction number R0 of 2.65 before lockdown, whereas R0 dropped to 1.98 for the first 30 days after the lockdown. The peak point is Feb. 17th, 2020 with 24,115 infected people and the end point is Jun. 17th, 2020. In total, 77,453 people will be infected. If lockdown imposed 7 days earlier, the total number of infected people would be 21,508, while delaying the lockdown by 1-6 days would expand the infection scale 1.23 to 4.94 times. A delay for 7 days would make the epidemic finally out of control. Doubling the number of beds in hospitals would decrease the total infections by 28%, and further investment in bed numbers would yield a diminishing return. Last, public gathering events that increased the transmission parameter by 5% in one single day would increase 4,243 infected people eventually.InterpretationOur model forecasted that the peak time in Wuhan was Feb. 17th, 2020 and the epidemic in Wuhan is now under control. The outbreak of SARS-CoV-2 is currently a global public health threat for all nations. Multiple countries including South Korea, Japan, Iran, Italy, and the United States are suffering from SARS-CoV-2. Our study, which simulated the epidemic in Wuhan, the first city in the world fighting against SARS-CoV-2, may provide useful guidance for other countries in dealing with similar situations.FundingNational Natural Science Foundation of China (31900483) and Shanghai Sailing program (19YF1441100).Research in contextEvidence before this studyThe epidemic of SARS-CoV-2 has been currently believed to started from Wuhan, China. The Chinese government started to report the data including infected, cured and dead since Jan 20th, 2020. We searched PubMed and preprint archives for articles published up to Feb 28th, 2020, which contained information about the Wuhan outbreak using the terms of “SARS-CoV-2”, “2019-nCoV”, “COVID-19”, “public health policies”, “coronavirus”, “CoV”, “Wuhan”, “transmission model”, etc. And a number of articles were found to forecast the early dynamics of the SARS-CoV-2 epidemic and clinical characteristics of COVID-19. Several of them mentioned the influence of city lockdown, whereas lacked research focused on revealing the impact of public health policies for the outbreak of SARS-CoV-2 through modeling study.Added value of this studyAs the first study systemically analysis the effect of three major public health policies including 1) lockdown of Wuhan City, 2) construction of temporary hospitals and 3) reduction of crowed gathering events in Wuhan city. The results demonstrated the epidemic in Wuhan from the potential first patient to the end point as well as the influence of public health policies are expected to provide useful guidance for other countries in fighting against the epidemic of SRAS-CoV-2.Implications of all the available evidenceAvailable evidence illustrated the human-to-human transmission of SARS-CoV-2, in which the migration of people in China during the epidemic may quickly spread the epidemic to the rest of the nation. These findings also suggested that the lockdown of Wuhan city may slow down the spread of the epidemic in the rest of China.

scholarly journals Estimation of Tunisia COVID-19 infected cases based on mortality rate

2020 ◽  
Author(s):  
Ines Abdeljaoued-Tej ◽  
Marc Dhenain
Keyword(s):  
Public Health ◽  
Mortality Rate ◽  
Health Policies ◽  
Public Health Policies ◽  
Infected People ◽  
Pessimistic Scenario ◽  
The Impact

ABSTRACTEstimating the number of people affected by COVID-19 is crucial in deciding which public health policies to follow. The authorities in different countries carry out mortality counts. We propose that the mortality reported in each country can be used to create an index of the number of actual cases at a given time. The specificity of whether or not deaths are rapid or not by COVID-19 also affects the number of actual cases. The number of days between the declaration of illness and death varies between 12 and 18 days. For a delay of 18 days, and using an estimated mortality rate of 2%, the number of cases in April 2020 in Tunisia would be 5 580 people. The pessimistic scenario predicts 22 320 infected people, and the most optimistic predicts 744 (which is the number of reported cases on April 12, 2020). Modeling the occurrence of COVID-19 cases is critical to assess the impact of policies to prevent the spread of the virus.

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