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in category health policy

399 results found. For more information, click each entry to expand.

61: Real-time time-series modelling for prediction of COVID-19 spread and intervention assessment
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Posted 29 Apr 2020

Real-time time-series modelling for prediction of COVID-19 spread and intervention assessment
1,222 downloads medRxiv health policy

Taha Hossein Rashidi, Siroos Shahriari, AKM Azad, Fatemeh Vafaee

Substantial amount of data about the COVID-19 pandemic is generated every day. Yet, data streaming, while considerably visualized, is not accompanied with advanced modelling techniques to provide real-time insights. This study introduces a unified platform which integrates visualization capabilities with advanced statistical methods for predicting the virus spread in the short run, using real-time data. The platform is backed up by advanced time series models to capture any possible non-linearity in the data which is enhanced by the capability of measuring the expected impact of preventive interventions such as social distancing and lockdowns. The platform enables lay users, and experts, to examine the data and develop several customized models with different restriction such as models developed for specific time window of the data. Our policy assessment of the case of Australia, shows that social distancing and travel ban restriction significantly affect the reduction of number of cases, as an effective policy.

62: Lockdown measures in response to COVID-19 in Sub-Saharan Africa: A rapid study of nine countries
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Posted 11 Jul 2020

Lockdown measures in response to COVID-19 in Sub-Saharan Africa: A rapid study of nine countries
1,176 downloads medRxiv health policy

Najmul Haider, Abdinasir Yusuf Osman, Audrey Gadzekpo, George O. Akpede, Danny Asogun, Rashid Ansumana, Richard J Lessells, Palwasha Khan, Muzamil Mahdi Abdel Hamid, Dorothy Yeboah-Manu, Leonard Mboera, Elizabeth H Shayo, Blandina Mmbaga, Mark Urassa, David Musoke, Nathan Kapata, Rashida Abbas Ferrand, Pascalina Chanda-Kapata, Florian Stigler, Thomas Czypionka, Richard A Kock, David McCoy

Lockdown measures have been introduced worldwide to contain the transmission of COVID-19. This paper defines the term lockdown and describes the design, timing and implementation of lockdown in nine countries in Sub Saharan Africa: Ghana, Nigeria, South Africa, Sierra Leone, Sudan, Tanzania, Uganda, Zambia and Zimbabwe. It also discusses the manner in which lockdown is enforced, the need to mitigate the harms of lockdown, and the association between lockdown and the reported number of COVID-19 cases and deaths. While there are some commonalities in the implementation of lockdown, a more notable finding is the variation in the design, timing and implementation of lockdown measures across the nine countries. We found that the number of reported cases is heavily dependent on the number of tests done, and that testing rates ranged from 9 to 21,261 per million population. The reported number of COVID-19 deaths per million population also varies, but is generally low when compared to countries in Europe and North America. While lockdown measures may have helped inhibit some community transmission, the pattern and nature of the epidemic remains unclear. Of concern are signs of lockdown harming health by affecting the functioning of the health system and causing social and economic harms. This paper highlights the need for inter-sectoral and trans-disciplinary research capable of providing a rigorous and holistic assessment of the harms and benefits of lockdown.

63: Predicting illness trajectory and hospital resource utilization of COVID-19 hospitalized patients - a nationwide study
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Posted 07 Sep 2020

Predicting illness trajectory and hospital resource utilization of COVID-19 hospitalized patients - a nationwide study
1,174 downloads medRxiv health policy

Michael Roimi, Rom Gutman, Jonathan Somer, Asaf Ben Arie, Ido Calman, Yaron Bar-Lavie, Udi Gelbshtein, Sigal Liverant-Taub, Arnona Ziv, Danny Eytan, Malka Gorfine, Uri Shalit

Background: The spread of COVID-19 has led to a severe strain on hospital capacity in many countries. There is a need for a model to help planners assess expected COVID-19 hospital resource utilization. Methods: Retrospective nationwide cohort study following the day-by-day clinical status of all hospitalized COVID-19 patients in Israel from March 1st to May 2nd, 2020. Patient clinical course was modelled with a machine learning approach based on a set of multistate Cox regression-based mod- els with adjustments for right censoring, recurrent events, competing events, left truncation, and time-dependent covariates. The model predicts the patient's entire disease course in terms of clinical states, from which we derive the patient's hospital length-of-stay, length-of-stay in critical state, the risk of in-hospital mortality, and total and critical care hospital-bed utilization. Accuracy assessed over eight cross-validation cohorts of size 330, using per-day Mean Absolute Error (MAE) of predicted hospital utilization averaged over 64 days; and area under the receiver operating characteristics (AUROC) for individual risk of critical illness and in-hospital mortality, assessed on the first day of hospitalization. We present predicted hospital utilization under hypothetical incoming patient scenarios. Findings: During the study period, 2,703 confirmed COVID-19 patients were hospitalized in Israel. The per-day MAEs for total and critical-care hospital- bed utilization, were 4.72 {+/-} 1.07 and 1.68 {+/-} 0.40 respectively; the AUROCs for prediction of the probabilities of critical illness and in-hospital mortality were 0.88 {+/-} 0.04 and 0.96 {+/-} 0.04, respectively. We further present the impact of several scenarios of patient influx on healthcare system utilization, and provide an R software package for predicting hospital-bed utilization. Interpretation: We developed a model that, given basic easily obtained data as input, accurately predicts total and critical care hospital utilization. The model enables evaluating the impact of various patient influx scenarios on hospital utilization and planning ahead of hospital resource allocation.

64: The impact of keeping a religious beard in the COVID-19 pandemic: an online cross sectional survey study exploring experiences of male medical healthcare professionals
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Posted 04 Nov 2020

The impact of keeping a religious beard in the COVID-19 pandemic: an online cross sectional survey study exploring experiences of male medical healthcare professionals
1,172 downloads medRxiv health policy

Amer Hamed, Asam Latif, Mohammad Haris, Sufyan Patel, Muhammad I Patel, Syed Abdur Rahman Mustafa, Omeair Khan, Ahmad Shoaib, Salman Waqar

There has been a disproportionate effect on individuals from black Asian & Minority Ehnic (BAME) in the UK in Coronavirus disease (COVID-19) pandemic, especially in the NHS staff. Many of them have been asked to remove their beard to be eligible to do the fit test which can have negative implications on their spiritual, psychological & emotional wellbeing. This paper surveyed the responses of 469 healthcare professionals (HCPs) with beards regarding the challenges they face in regard to personal protective equipment (PPE), mask fit testing and attitude of employers & colleagues. Professional discrimination through fit testing rejection, unavailability or inadequate PPE and the pressure to shave their beard are unpleasant outcomes of this pandemic for some of the NHS staff. NHS trusts & hospitals need to adjust their practices to include staff with beard in their COVID-19 arrangements.

65: How do Covid-19 policy options depend on end-of-year holiday contacts in Mexico City Metropolitan Area? A Modeling Study
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Posted 22 Dec 2020

How do Covid-19 policy options depend on end-of-year holiday contacts in Mexico City Metropolitan Area? A Modeling Study
1,164 downloads medRxiv health policy

Fernando Alarid-Escudero, Valeria Gracia, Andrea Luviano, Yadira Peralta, Marissa B Reitsma, Anneke L. Claypool, Joshua Salomon, David M. Studdert, Jason R Andrews, Jeremy D Goldhaber-Fiebert, Stanford-CIDE Coronavirus Simulation Model (SC-COSMO) Modeling Consortium

BackgroundWith more than 20 million residents, Mexico City Metropolitan Area (MCMA) has the largest number of Covid-19 cases in Mexico and is at risk of exceeding its hospital capacity in late December 2020. MethodsWe used SC-COSMO, a dynamic compartmental Covid-19 model, to evaluate scenarios considering combinations of increased contacts during the holiday season, intensification of social distancing, and school reopening. Model parameters were derived from primary data from MCMA, published literature, and calibrated to time-series of incident confirmed cases, deaths, and hospital occupancy. Outcomes included projected confirmed cases and deaths, hospital demand, and magnitude of hospital capacity exceedance. FindingsFollowing high levels of holiday contacts even with no in-person schooling, we predict that MCMA will have 1{middle dot}0 million (95% prediction interval 0{middle dot}5 - 1{middle dot}7) additional Covid-19 cases between December 7, 2020 and March 7, 2021 and that hospitalizations will peak at 35,000 (14,700 - 67,500) on January 27, 2021, with a >99% chance of exceeding Covid-19-specific capacity (9,667 beds). If holiday contacts can be controlled, MCMA can reopen in-person schools provided social distancing is increased with 0{middle dot}5 million (0{middle dot}2 - 1{middle dot}0) additional cases and hospitalizations peaking at 14,900 (5,600 - 32,000) on January 23, 2021 (77% chance of exceedance). InterpretationMCMA must substantially increase Covid-19 hospital capacity under all scenarios considered. MCMAs ability to reopen schools in mid-January 2021 depends on sustaining social distancing and that contacts during the end-of-year holiday were well controlled. FundingSociety for Medical Decision Making, Gordon and Betty Moore Foundation, and Wadhwani Institute for Artificial Intelligence Foundation. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSAs of mid-December 2020, Mexico has the twelfth highest incidence of confirmed cases of Covid-19 worldwide and its epidemic is currently growing. Mexicos case fatality ratio (CFR) - 9{middle dot}1% - is the second highest in the world. With more than 20 million residents, Mexico City Metropolitan Area (MCMA) has the highest number and incidence rate of Covid-19 confirmed cases in Mexico and a CFR of 8{middle dot}1%. MCMA is nearing its current hospital capacity even as it faces the prospect of increased social contacts during the 2020 end-of-year holidays. There is limited Mexico-specific evidence available on epidemic, such as parameters governing time-dependent mortality, hospitalization and transmission. Literature searches required supplementation through primary data analysis and model calibration to support the first realistic model-based Covid-19 policy evaluation for Mexico, which makes this analysis relevant and timely. Added value of this studyStudy strengths include the use of detailed primary data provided by MCMA; the Bayesian model calibration to enable evaluation of projections and their uncertainty; and consideration of both epidemic and health system outcomes. The model projects that failure to limit social contacts during the end-of-year holidays will substantially accelerate MCMAs epidemic (1{middle dot}0 million (95% prediction interval 0{middle dot}5 - 1{middle dot}7) additional cases by early March 2021). Hospitalization demand could reach 35,000 (14,700 - 67,500), with a >99% chance of exceeding current capacity (9,667 beds). Controlling social contacts during the holidays could enable MCMA to reopen in-person schooling without greatly exacerbating the epidemic provided social distancing in both schools and the community were maintained. Under all scenarios and policies, current hospital capacity appears insufficient, highlighting the need for rapid capacity expansion. Implications of all the available evidenceMCMA officials should prioritize rapid hospital capacity expansion. MCMAs ability to reopen schools in mid-January 2021 depends on sustaining social distancing and that contacts during the end-of-year holiday were well controlled.

66: COVID-19 OUTBREAK IN POST-SOVIET STATES: MODELING THE BEST AND WORST POSSIBLE SCENARIOS
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Posted 23 Apr 2020

COVID-19 OUTBREAK IN POST-SOVIET STATES: MODELING THE BEST AND WORST POSSIBLE SCENARIOS
1,156 downloads medRxiv health policy

Alpamys Issanov, Yerlan Amanbek, Anara Abbay, Shalkar Adambekov, Mohamad Aljofan, Ardak Kashkynbayev, Abduzhappar Gaipov

Background: COVID-19 pandemic has presented extreme challenges to developing countries across the world. Post-Soviet states are facing unique challenges due to their developing healthcare systems and unstable economy. The aim of this paper was to provide estimates for current development COVID-19 pandemic in the Post-Soviet states and forecast potential best and worst scenarios for spread of this deadly infection. Methods: The data on confirmed cases and deaths were extracted from official governmental sources for a period from beginning of outbreak dates for each country until April 18, 2020. A modified SEIR (Susceptible-Exposed-Infected-Recovered) modelling was used to plot the parameters of epidemic in 10 post-Soviet states and forecast the number of cases over a period of 10, 30 and 60 days. We also estimated the numbers of cases based on the optimal measures (best scenario) and suboptimal measures (worst scenarios) of potential spread of COVID-19 in these countries. Results: It was estimated that Armenia and Azerbaijan have reached their peaks, Kazakhstan, Kyrgyzstan, Moldova and Uzbekistan are expected to reach their peaks in the coming week (April 29 - May 7, 2020), with comparatively low cases of COVID-19 and loss of lives in the best-case scenario. In contrast, Belarus, Russia, and Ukraine would likely see the outbreaks with the largest number of COVID-19 cases amongst the studied Post-Soviet States in the worst scenario during the next 30 and 60 days. Geographical remoteness and small number of international travelers from the countries heavily affected by the pandemic could also have contributed to delay in the spread of COVID-19. Conclusion: Governmental response was shown to be as an important determining factor responsible for the development of COVID-19 epidemic in Post-Soviet states. The current protection rates should be maintained to reduce active cases during upcoming 30 and 60 days. The estimated possible scenarios based on the proposed model can potentially be used by healthcare professionals from each studied Post-Soviet States as well as others to improve plans to contain the current and future epidemic.

67: Gender disparities in international COVID-19 clinical trial leadership
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Posted 04 Aug 2020

Gender disparities in international COVID-19 clinical trial leadership
1,155 downloads medRxiv health policy

Muge Cevik, Syed Arefinul Haque, Jennifer Manne-Goehler, Krutika Kuppalli, Paul E. Sax, Maimuna S Majumder, Chloe M Orkin

Emerging data suggest that despite an increased number of peer-reviewed articles submitted to journals during the pandemic, women have published fewer papers than men thus far this year. In this study, we provide timely analysis to compare the gender distribution of clinical trial leadership in COVID-19 clinical trials. We demonstrate that less than one-third of COVID-19-related clinical trials are led by principal investigators who were predicted to be women, half the proportion observed in non-COVID-19 (breast cancer and T2DM) trials over the same period. These gender disparities during the pandemic may indicate not only a lack of women's leadership in international clinical trials and involvement in new projects but also may reveal imbalances in women's access to research activities and funding during health emergencies. The COVID-19 pandemic offers numerous opportunities for research and leadership that could equalize opportunity in a new field, but our results suggest the opposite. Our demonstration of gender differences in trial leadership and grant allocation argue for revised policies and strategies that encourage the participation of women in pandemic research. Not only can these women drive discovery and innovation, but they can act to address health disparities and provide role models for the next generation of women scientists.

68: Intensive Care Unit Resource Planning During COVID-19 Emergency at the Regional Level: the Italian case.
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Posted 20 Mar 2020

Intensive Care Unit Resource Planning During COVID-19 Emergency at the Regional Level: the Italian case.
1,125 downloads medRxiv health policy

Pietro Hiram Guzzi, Giuseppe Tradigo, Pierangelo Veltri

Severe acute respiratory syndrome COVID-19 (SARS-CoV-2) has been declared a worldwide emergency and a pandemic disease by the World Health Organisation (WHO). It started in China in December 2019, and it is currently rapidly spreading throughout Italy, which is now the most affected country after China. There is great attention for the diffusion and evolution of the COVID-19 infection which started from the north (particularly in the Lombardia region) and it is now rapidly affecting other Italian regions. We investigate on the impact of patients hospitalisation in Intensive Care Units (ICUs) at a regional and subregional granularity. We propose a model derived from well-known models in epidemic to estimate the needed number of places in intensive care units. The model will help decision-makers to plan resources in the short and medium-term in order to guarantee appropriate treatments to all patients needing it. We analyse Italian data at regional level up to March 15th aiming to: (i) support health and government decision-makers in gathering rapid and efficient decisions on increasing health structures capacities (in terms of ICU slots) and (ii) define a scalable geographic model to plan emergency and future COVID-19 patients management using reallocating them among health structures. Finally, the here proposed model can be useful in countries where COVID-19 is not yet strongly diffused.

69: Learning as We Go: An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions
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Posted 17 Apr 2020

Learning as We Go: An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions
1,109 downloads medRxiv health policy

Roman Marchant, Noelle I Samia, Ori Rosen, Martin A Tanner, Sally Cripps

A recent model developed at the Institute for Health Metrics and Evaluation (IHME) provides forecasts for ventilator use and hospital beds required for the care of COVID19 patients on a state-by-state basis throughout the United States over the period March 2020 through August 2020 (See the related website https://covid19.healthdata.org/projections for interactive data visualizations). In addition, the manuscript and associated website provide projections of deaths per day and total deaths throughout this period for the entire US, as well as for the District of Columbia. This research has received extensive attention in social media, as well as in the mass media. Moreover, this work has influenced policy makers at the highest levels of the United States government, having been mentioned at White House Press conferences, including March 31, 2020. In this paper, we evaluate the predictive validity of model forecasts for COVID19 outcomes as data become sequentially available, using the IHME prediction of daily deaths. We have found that the predictions for daily number of deaths provided by the IHME model have been highly inaccurate. The model has been found to perform poorly even when attempting to predict the number of next day deaths. In particular, the true number of next day deaths has been outside the IHME prediction intervals as much as 70% of the time.

70: Revealing the influence of national public health policies for the outbreak of the SARS-CoV-2 epidemic in Wuhan, China through status dynamic modeling
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Posted 12 Mar 2020

Revealing the influence of national public health policies for the outbreak of the SARS-CoV-2 epidemic in Wuhan, China through status dynamic modeling
1,098 downloads medRxiv health policy

Tianyi Qiu, Han Xiao

BackgroundThe 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 contextO_ST_ABSEvidence before this studyC_ST_ABSThe 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.

71: Do Men and Women Lockdown Differently? An Examination of Panamas COVID-19 Sex-Segregated Social Distancing Policy
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Posted 03 Jul 2020

Do Men and Women Lockdown Differently? An Examination of Panamas COVID-19 Sex-Segregated Social Distancing Policy
1,091 downloads medRxiv health policy

Liana R Woskie, Clare Wenham

Mobility enables individuals to generate income and is a key input for empowerment and personal autonomy. Curtailment of aggregate social mobility - through policies such as: social distancing recommendations, shelter in place orders and state-enforced lockdowns - has become a primary strategy to address COVID-19 to limit social contact and reduce disease transmission. In this context, a small number of countries have instituted gender or sex-segregated mobility policies (Panama, Peru, and Bogota, Colombia). Through a retrospective analysis of global geographic positioning (GPS) data, this study presents an overview of aggregate mobility in Panama following the countrys implementation of a sex-segregated social distancing policy. Panama was selected as the nationwide sex-segregated policy was enforced throughout the lockdown period. The paper looks at mobility trends on female- and male-sex mobility days, examining differences by volume of movement and type of community locations visited as compared to pre-COVID trends. We find lower visits to all community location categories on female-mobility days. However, we found no significant difference in visits to workplace locations on male- v. female-mobility days. The paper discusses the implications of these findings in three areas: (1) Informal burden of labor and social reproduction, (2) Implications for womens autonomy and safety in the home, and (3) Womens economic empowerment. In addition, it raises open ethical questions regarding gender identity in COVID-19 policies.

72: Evidence of the effectiveness of travel-related measures during the early phase of the COVID-19 pandemic: a rapid systematic review
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Posted 24 Nov 2020

Evidence of the effectiveness of travel-related measures during the early phase of the COVID-19 pandemic: a rapid systematic review
1,081 downloads medRxiv health policy

Karen Ann Grepin, Tsi-Lok Ho, Zhihan Liu, Summer Marion, Julianne Piper, Catherin Z Worsnop, Kelley Lee

Objective To review evidence of the effectiveness of travel measures implemented during the early stages of the COVID-19 pandemic in order to recommend change on how evidence is incorporated in the International Health Regulations (2005) (IHR). Design We used an abbreviated preferred reporting items for systematic reviews and meta-analysis protocol (PRISMA-P) and a search strategy aimed to identify studies that investigated the effectiveness of travel-related measures (advice, entry and exit screening, medical examination or vaccination requirements, isolation or quarantine, the refusal of entry, and entry restrictions), pre-printed or published by June 1, 2020. Results We identified 29 studies, of which 26 were modelled (vs. observational). Thirteen studies investigated international measures while 17 investigated domestic measures (one investigated both), including suspended transportation (24 studies), border restrictions (21), and screening (5). There was a high level of agreement that the adoption of travel measures led to important changes in the dynamics of the early phases of the COVID-19 pandemic. However, most of the identified studies investigated the initial export of cases out of Wuhan, which was found to be highly effective, but few studies investigated the effectiveness of measures implemented in other contexts. Early implementation was identified as a determinant of effectiveness. Most studies of international travel measures failed to account for domestic travel measures, and thus likely led to biased estimates. Poor data and other factors contributed to the low quality of the studies identified. Conclusion Travel measures, especially those implemented in Wuhan, played a key role in shaping the early transmission dynamics of the COVID-19 pandemic, however, the effectiveness of these measures was short-lived. There is an urgent need to address important evidence gaps, but also a need to review the way in which evidence is incorporated in the IHR in the early phases of a novel infectious disease outbreak.

73: Outbreak analysis with a logistic growth model shows COVID-19 suppression dynamics in China
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Posted 27 Mar 2020

Outbreak analysis with a logistic growth model shows COVID-19 suppression dynamics in China
1,080 downloads medRxiv health policy

Yi Zou, Stephen Pan, Peng Zhao, Lei Han, Xiaoxiang Wang, Lia Hemerik, Johannes M H Knops, Wopke van der Werf

China experienced an outbreak of a novel coronavirus, SARS-CoV2, from mid-January till mid-March 2020. Here we review the curves of epidemic growth and decline of the virus in China using a phenomenological logistic growth model to summarize the dynamics of the outbreak using three parameters that characterize the epidemics timing, rate and peak. During the initial phase, the number of cases doubled every 2.7 (range 2.2 - 4.4) days. The rate of increase in the number of reported cases peaked approximately 10 days after suppression measures were started on 23-25 January 2020. The peak in the number of reported sick cases occurred on average 18 days after the start of measures. From the time of starting measures till the peak, the number of cases increased by a factor 38.5 in the province Hubei, and by a factor 9.5 for all of China (range: 6.2-20.4 in the other provinces). Complete suppression took up to 2 months (range: 23-57d.), during which period severe restrictions, social distancing measures, testing and isolation of cases were in place. The suppression of the disease in China has been successful, acting as a beacon of hope for countries outside China where the epidemic is still in a phase of increase and authorities need to decide their course of action.

74: Sustaining Social Distancing Policies to Prevent a Dangerous Second Peak of COVID-19 Outbreak
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Posted 22 Apr 2020

Sustaining Social Distancing Policies to Prevent a Dangerous Second Peak of COVID-19 Outbreak
1,073 downloads medRxiv health policy

Zhilan Feng, Haiyun Damon-Feng, Henry Zhao

Governments around the world have enacted strict social distancing policies in order to slow the spread of COVID-19. The next step is figuring out when to relax these restrictions and to what degree. Our results predict potentially disastrous implications of ending these policies too soon, based on projections made from a Susceptible-Exposed-Infectious-Removed (SEIR) epidemic model. Even when infection rates appear to be slowing down or decreasing, prematurely returning to "business as usual" produces a severe second peak far worse than the first. Furthermore, such a second peak is made more likely when very severe restrictions are initially enacted. Only an appropriately measured and committed set of restrictions can appropriately control COVID-19 outbreak levels.

75: The Effect of NFL and NCAA Football Games on the Spread of COVID-19 in the United States: An Empirical Analysis
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Posted 19 Feb 2021

The Effect of NFL and NCAA Football Games on the Spread of COVID-19 in the United States: An Empirical Analysis
1,054 downloads medRxiv health policy

Asmae Toumi, Haoruo Zhao, Jagpreet Chhatwal, Benjamin P Linas, Turgay Ayer

ImportanceIn 2020 and early 2021, the National Football League (NFL) and National Collegiate Athletic Association (NCAA) had opted to host games in stadiums across the country. The in-person attendance of games has varied with time and from county to county. There is currently no evidence on whether limited in-person attendance of games has caused a substantial increase in coronavirus disease 2019 (COVID-19) cases. ObjectiveTo assess whether NFL and NCAA football games with limited in-person attendance have contributed to a substantial increase in COVID-19 cases in the counties they were held. DesignIn this time-series cross-sectional study, we matched every county hosting game(s) with in-person attendance (treated) in 2020 and 2021 with a county that has an identical game history for up to 14 days (control). We employed a standard matching method to further refine this matched set so that the treated and matched control counties have similar population size, non-pharmaceutical intervention(s) in place, and COVID-19 trends. We assessed the effect of hosting games with in-person attendance using a difference-in-difference estimator. SettingU.S. counties. ParticipantsU.S. counties hosting NFL and/or NCAA games. ExposureHosting NFL and/or NCAA games. Main outcomes and measuresEstimating the impact of NFL and NCAA games with in-person attendance on new, reported COVID-19 cases per 100,000 residents at the county-level up to 14 days post-game. ResultsThe matching algorithm returned 361 matching sets of counties. The effect of in-person attendance at NFL and NCAA games on community COVID-19 spread is not significant as it did not surpass 5 new daily cases of COVID-19 per 100,000 residents on average. Conclusions and relevanceThis time-series, cross-sectional matching study with a difference-in-differences design did not find an increase in COVID-19 cases per 100,000 residents in the counties where NFL and NCAA games were held with in-person attendance. Our study suggests that NFL and NCAA football games hosted with limited in-person attendance do not cause a significant increase in local COVID-19 cases. Key pointsO_ST_ABSQuestionC_ST_ABSDid NFL and NCAA football games with limited in-person attendance cause a substantia increase in coronavirus disease 2019 (COVID-19) cases in the U.S. counties where the games were held? FindingsThis time-series, cross-sectional study of U.S. counties with NFL and NCAA football games used matching and difference-in-differences design to estimate the effect of games with limited in-person attendance on county-level COVID-19 spread. Our study does not find an increase in county-level COVID-19 cases per 100,000 residents due to NFL and NCAA football games held with limited in-person attendance. MeaningThis study suggests that NFL and NCAA games held with limited in-person attendance do not cause an increase in COVID-19 cases in the counties they are held.

76: COVID-19 transmission in a university setting: a rapid review of modelling studies
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Posted 09 Sep 2020

COVID-19 transmission in a university setting: a rapid review of modelling studies
1,036 downloads medRxiv health policy

Hannah Christensen, Katy Turner, Adam Trickey, Ross. D. Booton, Gibran Hemani, Emily J Nixon, Caroline Relton, Leon Danon, Matthew Hickman, Ellen Brooks Pollock

Managing COVID-19 within a university setting presents unique challenges. At the start of term, students arrive from geographically diverse locations and potentially have higher numbers of social contacts than the general population, particularly if living in university halls of residence accommodation. Mathematical models are useful tools for understanding the potential spread of infection and are being actively used to inform policy about the management of COVID-19. Our aim was to provide a rapid review and appraisal of the literature on mathematical models investigating COVID-19 infection in a university setting. We searched PubMed, Web of Science, bioRxiv/ medRxiv and sought expert input via social media to identify relevant papers. BioRxiv/ medRxiv and PubMed/Web of Science searches took place on 3 and 6 July 2020, respectively. Papers were restricted to English language. Screening of peer-reviewed and pre-print papers and contact with experts yielded five relevant papers - all of which were pre-prints. All models suggest a significant potential for transmission of COVID-19 in universities. Testing of symptomatic persons and screening of the university community regardless of symptoms, combined with isolation of infected individuals and effective contact tracing were critical for infection control in the absence of other mitigation interventions. When other mitigation interventions were considered (such as moving teaching online, social/physical distancing, and the use of face coverings) the additional value of screening for infection control was limited. Multiple interventions will be needed to control infection spread within the university setting and the interaction with the wider community is an important consideration. Isolation of identified cases and quarantine of contacts is likely to lead to large numbers of students requiring educational, psychological and behavioural support and will likely have a large impact on the attendance of students (and staff), necessitating online options for teaching, even where in-person classes are taking place. Models were highly sensitive to assumptions in the parameters, including the number and type of individuals contacts, number of contacts traced, frequency of screening and delays in testing. Future models could aid policy decisions by considering the incremental benefit of multiple interventions and using empirical data on mixing within the university community and with the wider community where available. Universities will need to be able to adapt quickly to the evolving situation locally to support the health and wellbeing of the university and wider communities.

77: Hospital length of stay for severe COVID-19: implications for Remdesivir's value
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Posted 12 Aug 2020

Hospital length of stay for severe COVID-19: implications for Remdesivir's value
1,011 downloads medRxiv health policy

Michaela Anderson, Peter Bach, Matthew R Baldwin

Remdesivir has been granted emergency use authorization for treatment of severe COVID-19. Remdesivir's pricing is based on a presumed reduction of hospital length of stay (LOS) by four days. But the Adaptive COVID-19 Treatment Trial (ACTT-1) that suggested this treatment benefit excluded patients who were expected to be discharged within 72 hours. Perhaps as a result, median time to recovery was unusually long in both arms of the study (15 days vs 11 days). Remdesivir requires a 5-day inpatient stay, so patients who would otherwise be discharged in fewer than 5 days may remain hospitalized to complete treatment while patients who would be discharged between 5 and 8 days, would only have potential reductions in their hospital LOS of 0-3 days. In a retrospective analysis of 1643 adults with severe COVID-19 admitted to Columbia University Medical Center and the Allen community hospital between March 9, 2020 and April 23, 2020, median hospital LOS was 7 (3-14) days. Five-hundred and eighty-six patients (36%) had a LOS of 1-4 days, 384 (23%) had a LOS of 5-8 days, and 673 (41%) were hospitalized for greater than or equal to 9 days. Remdesivir treatment may not provide the LOS reductions that the company relied on when pricing the therapy: 36% of the cohort would need to have LOS prolonged to receive a 5-day course, and only 41% of patients in our cohort had LOS of 9 days or more, meaning they could have their LOS shortened by 4 days and still receive a full Remdesivir course. Further investigation of shorter treatment courses and programs to facilitate outpatient intravenous Remdesivir administration are needed.

78: Supporting COVID-19 Policy-Making with a Predictive Epidemiological Multi-Model Warning System
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Posted 20 Oct 2020

Supporting COVID-19 Policy-Making with a Predictive Epidemiological Multi-Model Warning System
1,007 downloads medRxiv health policy

Martin Bicher, Martin Zuba, Lukas Rainer, Florian Bachner, Claire Rippinger, Herwig Ostermann, Nikolas Popper, Stefan Thurner, Peter Klimek

In response to the SARS-CoV-2 pandemic, the Austrian governmental crisis unit commissioned a forecast consortium with regularly projections of case numbers and demand for hospital beds. The goal was to assess how likely Austrian ICUs would become overburdened with COVID-19 patients in the upcoming weeks. We consolidated the output of three independent epidemiological models (ranging from agent-based micro simulation to parsimonious compartmental models) and published weekly short-term forecasts for the number of confirmed cases as well as estimates and upper bounds for the required hospital beds. Here, we report on three key contributions by which our forecasting and reporting system has helped shaping Austria's policy to navigate the crisis, namely (i) when and where case numbers and bed occupancy are expected to peak during multiple waves, (ii) whether to ease or strengthen non-pharmaceutical intervention in response to changing incidences, and (iii) how to provide hospital managers guidance to plan health-care capacities. Complex mathematical epidemiological models play an important role in guiding governmental responses during pandemic crises, in particular when they are used as a monitoring system to detect epidemiological change points.

79: A COVID-19 Reopening Readiness Index: The Key to Opening up the Economy
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Posted 26 May 2020

A COVID-19 Reopening Readiness Index: The Key to Opening up the Economy
995 downloads medRxiv health policy

MAHDI ALHAERY, eunju suh

With respect to reopening the economy as a result of the COVID-19 restrictions, governmental response and messaging have been inconsistent, and policies have varied by state as this is a uniquely polarizing topic. Considering the urgent need to return to normalcy, a method was devised to determine the degree of progress any state has made in containing the spread of COVID-19. Using various measures for each state including mortality, hospitalizations, testing capacity, number of infections and infection rate has allowed for the creation of a composite COVID -19 Reopening Readiness Index. This index can serve as a comprehensive reliable and simple-to-use metric to assess the level of containment in any state and to determine the level of risk in further opening. As states struggle to contain the outbreak and at the same time face great pressure in resuming economic activity, an index that provides a data-driven and objective insight is urgently needed.

80: Examining COVID19 Positivity-Ratio Trends in US States from April-July: Are Rising Caseloads Attributable to <More Testing> and Do State Political-Affiliations Play a Role?
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Posted 22 Jul 2020

Examining COVID19 Positivity-Ratio Trends in US States from April-July: Are Rising Caseloads Attributable to <More Testing> and Do State Political-Affiliations Play a Role?
994 downloads medRxiv health policy

Bisakha Pia Sen, Sangeetha Padalabalanarayanan

Importance: Rising Covid19 cases in the US are attributed by some political leaders to more testing. Positivity-ratios (cases to tests ratio) in conjunction with cases and tests provide a better overview. However, comprehensive overviews of positivity-ratio patterns are scarce. Objective: To examine trends in positivity-ratios, tests and cases by state from mid-April-mid-July. Further, to examine whether positivity-ratio patters are associated with state political-affiliations. Methods: State-level publicly available data on Covid19 is used. Seven-day moving averages (MA7) of positivity-ratio are computed for April 21-July 15. States are assigned to four groups based on patterns of change in positivity-ratio: higher at end of study period than beginning (Group 1), initial decline but subsequent increase starting Memorial Day weekend (Group 2), initial decrease but an upturn in last 14 days (Group 3), and consistent downward trend (Group 4). Political-affiliation is categorized as <Republican-leaning> if President Trump won the state and the governor is Republican. Additionally, a proxy measure is used to indicate intensity of Black Lives Matter (BLM) protests in the state. Associations are tested using chi-square analysis. Results: Fourteen states are in Group 1, fifteen states in Group 2, fifteen states in Group 3, and six states and DC in Group 4. 78.57% of Group 1, 33.33% of Group 2, 40% of group 3, and none in Group 4 were Republican-leaning. The difference in distribution was statistically significant (p<0.01). Distribution of high-intensity BLM protests across the four groups was not statistically different (p>0.10). Conclusion: Increased Covid19 cases cannot be attributed to more testing. Indeed, the high positivity-ratios in most states indicate current testing is failing to capture actual infection rates. The association between state political-affiliation and positivity-ratios suggests Republican voters may be somewhat more skeptical of the gravity of the disease and emphasizes the importance of messaging by political leaders.

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