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1: Multi-organ impairment in low-risk individuals with long COVID
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Posted 16 Oct 2020

Multi-organ impairment in low-risk individuals with long COVID
79,382 downloads medRxiv health policy

Andrea Dennis, Malgorzata Wamil, Sandeep Kapur, Johann Alberts, Andrew D Badley, Gustav Anton Decker, Stacey A. Rizza, Rajarshi Banerjee, Amitava Banerjee, On behalf of the COVERSCAN study investigators

BackgroundSevere acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection has disproportionately affected older individuals and those with underlying medical conditions. Research has focused on short-term outcomes in hospital, and single organ involvement. Consequently, impact of long COVID (persistent symptoms three months post-infection) across multiple organs in low-risk individuals is yet to be assessed. MethodsAn ongoing prospective, longitudinal, two-centre, observational study was performed in individuals symptomatic after recovery from acute SARS-CoV-2 infection. Symptoms and organ function (heart, lungs, kidneys, liver, pancreas, spleen) were assessed by standardised questionnaires (EQ-5D-5L, Dyspnoea-12), blood investigations and quantitative magnetic resonance imaging, defining single and multi-organ impairment by consensus definitions. FindingsBetween April and September 2020, 201 individuals (mean age 44 (SD 11.0) years, 70% female, 87% white, 31% healthcare workers) completed assessments following SARS-CoV-2 infection (median 140, IQR 105-160 days after initial symptoms). The prevalence of pre-existing conditions (obesity: 20%, hypertension: 6%; diabetes: 2%; heart disease: 4%) was low, and only 18% of individuals had been hospitalised with COVID-19. Fatigue (98%), muscle aches (88%), breathlessness (87%), and headaches (83%) were the most frequently reported symptoms. Ongoing cardiorespiratory (92%) and gastrointestinal (73%) symptoms were common, and 42% of individuals had ten or more symptoms. There was evidence of mild organ impairment in heart (32%), lungs (33%), kidneys (12%), liver (10%), pancreas (17%), and spleen (6%). Single (66%) and multi-organ (25%) impairment was observed, and was significantly associated with risk of prior COVID-19 hospitalisation (p<0.05). InterpretationIn a young, low-risk population with ongoing symptoms, almost 70% of individuals have impairment in one or more organs four months after initial symptoms of SARS-CoV-2 infection. There are implications not only for burden of long COVID but also public health approaches which have assumed low risk in young people with no comorbidities. FundingThis work was supported by the UKs National Consortium of Intelligent Medical Imaging through the Industry Strategy Challenge Fund, Innovate UK Grant 104688, and also through the European Unions Horizon 2020 research and innovation programme under grant agreement No 719445.

2: Demographic science aids in understanding the spread and fatality rates of COVID-19
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Posted 18 Mar 2020

Demographic science aids in understanding the spread and fatality rates of COVID-19
15,377 downloads medRxiv health policy

Jennifer Beam Dowd, Liliana Andriano, Valentina Rotondi, David M. Brazel, Per Block, Xuejie Ding, Yan Liu, Melinda C Mills

Governments around the world must rapidly mobilize and make difficult policy decisions to mitigate the COVID-19 pandemic. Because deaths have been concentrated at older ages, we highlight the important role of demography, particularly how the age structure of a population may help explain differences in fatality rates across countries and how transmission unfolds. We examine the role of age structure in deaths thus far in Italy and South Korea and illustrate how the pandemic could unfold in populations with similar population sizes but different age structures, showing a dramatically higher burden of mortality in countries with older versus younger populations. This powerful interaction of demography and current age-specific mortality for COVID-19 suggests that social distancing and other policies to slow transmission should consider both the age composition of local and national contexts as well as the social connectedness of older and younger generations. We also call for countries to provide case and fatality data disaggregated by age and sex to improve real-time targeted nowcasting.

3: Effects of non-pharmaceutical interventions on COVID-19: A Tale of Three Models
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Posted 27 Jul 2020

Effects of non-pharmaceutical interventions on COVID-19: A Tale of Three Models
15,174 downloads medRxiv health policy

Vincent Chin, John Ioannidis, Martin Tanner, Sally Cripps

Objective: To compare the inference regarding the effectiveness of the various non-pharmaceutical interventions (NPIs) for COVID-19 obtained from different SIR models. Study design and setting: We explored two models developed by Imperial College that considered only NPIs without accounting for mobility (model 1) or only mobility (model 2), and a model accounting for the combination of mobility and NPIs (model 3). Imperial College applied models 1 and 2 to 11 European countries and to the USA, respectively. We applied these models to 14 European countries (original 11 plus another 3), over two different time horizons. Results: While model 1 found that lockdown was the most effective measure in the original 11 countries, model 2 showed that lockdown had little or no benefit as it was typically introduced at a point when the time-varying reproductive number was already very low. Model 3 found that the simple banning of public events was beneficial, while lockdown had no consistent impact. Based on Bayesian metrics, model 2 was better supported by the data than either model 1 or model 3 for both time horizons. Conclusions: Inferences on effects of NPIs are non-robust and highly sensitive to model specification. Claimed benefits of lockdown appear grossly exaggerated.

4: Who funded the research behind the Oxford-AstraZeneca COVID-19 vaccine? - Approximating the funding to the University of Oxford for the research and development of the ChAdOx vaccine technology
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Posted 10 Apr 2021

Who funded the research behind the Oxford-AstraZeneca COVID-19 vaccine? - Approximating the funding to the University of Oxford for the research and development of the ChAdOx vaccine technology
14,996 downloads medRxiv health policy

Samuel Cross, Yeanuk Rho, Henna Reddy, Toby Pepperrell, Florence Rodgers, Rhiannon Osborne, Ayolola Eni-Olotu, Rishi Banerjee, Sabrina Wimmer, Sarai Mirjam Keestra

Objectives: The Oxford-AstraZeneca COVID-19 vaccine (ChAdOx1 nCoV-19 or Vaxzevira) builds on nearly two decades of research and development (R&D) into Chimpanzee adenovirus-vectored vaccine (ChAdOx) technology at the University of Oxford. This study aims to approximate the funding for the R&D of the ChAdOx technology and the Oxford-AstraZeneca vaccine, and assess the transparency of funding reporting mechanisms. Design: We conducted a scoping review and publication history analysis of the principal investigators to reconstruct the funding for the R&D of the ChAdOx technology. We matched award numbers with publicly-accessible grant databases. We filed Freedom Of Information (FOI) requests to the University of Oxford for the disclosure of all grants for ChAdOx R&D. Results: We identified 100 peer-reviewed articles relevant to ChAdOx technology published between 01/2002 and 10/2020, extracting 577 mentions of funding bodies from funding acknowledgement statements. Government funders from overseas were mentioned 158 (27.4%), the U.K. government 147 (25.5%) and charitable funders 138 (23.9%) times. Grant award numbers were identified for 215 (37.3%) mentions, amounts were available in the public realm for 121 (21.0%) mentions. Based on the FOIs, until 01/2020, the European Commision (34.0%), Wellcome Trust (20.4%) and CEPI (17.5%) were the biggest funders of ChAdOx R&D. From 01/2020, the U.K. Department of Health and Social Care was the single largest funder (89.3%). The identified R&D funding was GBP104,226,076 reported in the FOIs, and GBP228,466,771 reconstructed from the literature search. Conclusions: Our study identified that public funding accounted for 97.1-99.0% of the funding towards the R&D of ChAdOx and the Oxford-AstraZeneca vaccine. We furthermore encountered a severe lack of transparency in research funding reporting mechanisms.

5: State-wise estimates of current hospital beds, intensive care unit (ICU) beds and ventilators in India: Are we prepared for a surge in COVID-19 hospitalizations?
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Posted 18 Jun 2020

State-wise estimates of current hospital beds, intensive care unit (ICU) beds and ventilators in India: Are we prepared for a surge in COVID-19 hospitalizations?
13,822 downloads medRxiv health policy

Geetanjali Kapoor, Stephanie Hauck, Aditi Sriram, Jyoti Joshi, Emily Schueller, Isabel Frost, Ruchita Balasubramanian, Ramanan Laxminarayan, Arindam Nandi

Background The rapid spread of COVID-19 globally has prompted policymakers to evaluate the capacity of health care infrastructure in their communities. Many hard-hit localities have witnessed a large influx of severe cases that strained existing hospitals. As COVID-19 spreads in India, it is essential to evaluate the country's capacity to treat severe cases. Methods We combined data on public and private sector hospitals in India to produce state level estimates of hospital beds, ICU beds, and mechanical ventilators. Based on the number of public sector hospitals from the 2019 National Health Profile (NHP) of India and the relative proportions of public and private health care facilities from the National Sample Survey (NSS) 75th round (2017-2018), we estimated capacity in each Indian state and union territory (UT). We assumed that 5% of all hospital beds were ICU beds and that 50% of ICU beds were equipped with ventilators. Results We estimated that India has approximately 1.9 million hospital beds, 95,000 ICU beds and 48,000 ventilators. Nationally, resources are concentrated in the private sector (hospital beds: 1,185,242 private vs 713,986 public; ICU beds: 59,262 private vs 35,699 public; ventilators: 29,631 private vs. 17,850 public). Our findings suggest substantial variation in available resources across states and UTs. Conclusion Some projections shave suggested a potential need for approximately 270,000 ICU beds in an optimistic scenario, over 2.8 times the estimated number of total available ICU beds in India. Additional resources will likely be required to accommodate patients with severe COVID-19 infections in India.

6: COVID-19 Mitigation Practices and COVID-19 Rates in Schools: Report on Data from Florida, New York and Massachusetts
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Posted 21 May 2021

COVID-19 Mitigation Practices and COVID-19 Rates in Schools: Report on Data from Florida, New York and Massachusetts
10,934 downloads medRxiv health policy

Emily Oster, Rebecca Jack, Clare Halloran, John Schoof, Diana McLeod

This paper reports on the correlation of mitigation practices with staff and student COVID-19 case rates in Florida, New York, and Massachusetts during the 2020-2021 school year. We analyze data collected by the COVID-19 School Response Dashboard and focus on student density, ventilation upgrades, and masking. We find higher student COVID-19 rates in schools and districts with lower in-person density but no correlations in staff rates. Ventilation upgrades are correlated with lower rates in Florida but not in New York. We do not find any correlations with mask mandates. All rates are lower in the spring, after teacher vaccination is underway

7: Brazilian Modeling of COVID-19 (BRAM-COD): a Bayesian Monte Carlo approach for COVID-19 spread in a limited data set context
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Posted 03 May 2020

Brazilian Modeling of COVID-19 (BRAM-COD): a Bayesian Monte Carlo approach for COVID-19 spread in a limited data set context
10,025 downloads medRxiv health policy

Samy Dana, Alexandre B Simas, Bruno A Filardi, Rodrigo N Rodriguez, Leandro Lane da Costa Valiengo, Jose Gallucci-Neto

Background: The new coronavirus respiratory syndrome disease (COVID-19) pandemic has become a major health problem worldwide. Many attempts have been devoted to modeling the dynamics of new infection rates, death rates, and the impact of the disease on health systems and the world economy. Most of these modeling concepts use the Susceptible-Infectious-Susceptible (SIS) and Susceptible-Exposed-Infected-Recovered (SEIR) compartmental models; however, wide imprecise outcomes in forecasting can occur with these models in the context of poor data, low testing levels, and a nonhomogeneous population. Objectives: To predict Brazilian ICU beds demand over time and during COVID-19 pandemic peak. Methods: In the present study, we describe a Bayesian COVID-19 model combined with a Hamiltonian Monte Carlo algorithm to forecast quantitative predictions of infections, number of deaths and the demand for critical care beds in the next month in the Brazilian context of scarce data availability. We also estimated COVID-19 spread tendency in the state of Sao Paulo and forecasted the demand for critical care beds, as Sao Paulo is the epicenter of the Latin America pandemic. Results: Our model estimated that the number of infected individuals would be approximately 6.5 million (median) on April 25, 2020, and would reach 16 to 17 million (median) by the end of August 2020 in Brazil. The probability that an infected individual requires ICU-level care in Brazil is 0.5833% . Our model suggests that the current level of mitigation seen in Sao Paulo is sufficient to reach Rt < 1, thus attaining a peak in the short term. In Sao Paulo state, the total number of deaths is estimated to be around 9,000 (median) with the 2.5% quantile being 6,600 deaths and the 97.5% quantile being around 13,350 deaths. Also, Sao Paulo will not attain its maximum capacity of ICU beds if the current trend persists over the long term. Conclusions: The COVID-19 pandemic should peak in Brazil between May 8 and May 20, 2020 with a fatality rate lower than that suggested in the literature. The northern and northeastern regions of Brazil will suffer from a lack of available ICU beds, whereas the southeastern, southern, and central-western regions appear to have sufficient ICU beds only if they share private system beds with the publicly funded Unified Heath System (SUS). The model predicts that, if the current policies and population behavior are maintained throughout the forecasted period, by the end of August 2020, Brazil will have around 7.6% to 8.2% of its population immune to COVID-19.

8: Acceptance and Attitudes Toward COVID-19 Vaccines: A Cross-Sectional Study from Jordan
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Posted 24 Dec 2020

Acceptance and Attitudes Toward COVID-19 Vaccines: A Cross-Sectional Study from Jordan
10,022 downloads medRxiv health policy

Tamam El-Elimat, Mahmoud M. AbuAlSamen, Basima A. Almomani, Nour A. Al-Sawalha, Feras Q. Alali

BackgroundVaccines are effective interventions that can reduce the high burden of diseases globally. However, public vaccine hesitancy is a pressing problem for public health authorities. With the availability of COVID-19 vaccines, little information is available on the public acceptability and attitudes towards the COVID-19 vaccines in Jordan. This study aimed to investigate the acceptability of COVID-19 vaccines and its predictors in addition to the attitudes towards these vaccines among public in Jordan. MethodsAn online, cross-sectional, and self-administered questionnaire was instrumentalized to survey adult participants from Jordan on the acceptability of COVID-19 vaccines. Logistic regression analysis was used to find the predictors of COVID-19 vaccines acceptability. ResultsA total of 3,100 participants completed the survey. The public acceptability of COVID-19 vaccines was fairly low (37.4%) in Jordan. Males (OR=2.488, 95CI%=1.834-3.375, p<.001) and those who took the seasonal influenza vaccine (OR=2.036, 95CI%=1.306-3.174, p=.002) were more likely to accept Covid-19 vaccines. Similarly, participants who believed that vaccines are generally safe (OR=9.258, 95CI%=6.020-14.237, p<.001) and those who were willing to pay for vaccines (OR=19.223, 95CI%=13.665-27.042, p<.001), once available, were more likely to accept the COVID-19 vaccines. However, those above 35 years old (OR=0.376, 95CI%=0.233-0.607, p<.001) and employed participants (OR=0.542, 95CI%=0.405-0.725, p<.001) were less likely to accept the COVID-19 vaccines. Moreover, participants who believed that there was a conspiracy behind COVID-19 (OR=0.502, 95CI%=0.356- 0.709, p<.001) and those who do not trust any source of information on COVID-19 vaccines (OR=0.271, 95CI%=0.183 - 0.400, p<.001), were less likely to have acceptance towards them. The most trusted sources of information on COVID-19 vaccines were healthcare providers. ConclusionSystematic interventions are required by public health authorities to reduce the levels of vaccines hesitancy and improve their acceptance. We believe these results and specifically the low rate of acceptability is alarming to Jordanian health authorities and should stir further studies on the root causes and the need of awareness campaigns. These interventions should take the form of reviving the trust in national health authorities and structured awareness campaigns that offer transparent information about the safety and efficacy of the vaccines and the technology that was utilized in their production.

9: Pandemic Politics: Timing State-Level Social Distancing Responses to COVID-19
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Posted 31 Mar 2020

Pandemic Politics: Timing State-Level Social Distancing Responses to COVID-19
8,964 downloads medRxiv health policy

Christopher Adolph, Kenya Amano, Bree Bang-Jensen, Nancy Fullman, John Wilkerson

Social distancing policies are critical but economically painful measures to flatten the curve against emergent infectious diseases. As the novel coronavirus that causes COVID-19 spread throughout the United States in early 2020, the federal government issued social distancing recommendations but left to the states the most difficult and consequential decisions restricting behavior, such as canceling events, closing schools and businesses, and issuing stay-at-home orders. We present an original dataset of state-level social distancing policy responses to the epidemic and explore how political partisanship, COVID-19 caseload, and policy diffusion explain the timing of governors decisions to mandate social distancing. An event history analysis of five social distancing policies across all fifty states reveals the most important predictors are political: all else equal, Republican governors and governors from states with more Trump supporters were slower to adopt social distancing policies. These delays are likely to produce significant, on-going harm to public health.

10: Risk Stratification tool for Healthcare workers during the CoViD-19 Pandemic; using published data on demographics, co-morbid disease and clinical domain in order to assign biological risk
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Posted 09 May 2020

Risk Stratification tool for Healthcare workers during the CoViD-19 Pandemic; using published data on demographics, co-morbid disease and clinical domain in order to assign biological risk
8,661 downloads medRxiv health policy

Janusz Jankowski, Angharad davies, Peter English, Ellis Friedman, Helena McKeown, Mala Rao, Su Sethi, W David Strain

Healthcare workers have a greater exposure to individuals with confirmed SARS-novel coronavirus 2, and thus a higher probability of contracting coronavirus disease (CoViD)-19, than the general population. Employers have a duty of care to minimise the risk for their employees. Several bodies including the Faculty of Occupational Medicine, NHS Employers, and Public Health England have published a requirement to perform risk assessments for all health care workers, however, with the absence of an objective risk stratification tool, comparing assessments between individuals is difficult if not impossible. Using published data, we explored the predictive role of basic demographics such as age, sex, ethnicity and comorbidities in order to establish an objective risk stratification tool that could help risk allocate duties to health care workers. We developed an objective risk stratification tool using a Caucasian female <50years of age with no comorbidities as a reference. Each point allocated to risk factors was associated with an approximate doubling in risk. This tool was then validated against the primary care-based analysis. This tool provides objective support for employers when determining which healthcare workers should be allocated to high-risk vs. lower risk patient facing clinical duties or to remote supportive roles.

11: Women in power: Female leadership and public health outcomes during the COVID-19 pandemic
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Posted 15 Jul 2020

Women in power: Female leadership and public health outcomes during the COVID-19 pandemic
6,995 downloads medRxiv health policy

Luca Coscieme, Lorenzo Fioramonti, Lars F Mortensen, Kate Pickett, Ida Kubiszewski, Hunter Lovins, Jacqueline McGlade, Kristin Vala Ragnarsdottir, Debra Roberts, Robert Costanza, Roberto De Vogli, Richard Wilkinson

Some countries have been more successful than others at dealing with the COVID-19 pandemic. When we explore the different policy approaches adopted as well as the underlying socio-economic factors, we note an interesting set of correlations: countries led by women leaders have fared significantly better than those led by men on a wide range of dimensions concerning the global health crisis. In this paper, we analyze available data for 35 countries, focusing on the following variables: number of deaths per capita due to COVID-19, number of days with reported deaths, peaks in daily deaths, deaths occurred on the first day of lockdown, and excess mortality. Results show that countries governed by female leaders experienced much fewer COVID-19 deaths per capita and were more effective and rapid at flattening the epidemic's curve, with lower peaks in daily deaths. We argue that there are both contingent and structural reasons that may explain these stark differences. First of all, most women-led governments were more prompt at introducing restrictive measures in the initial phase of the epidemic, prioritizing public health over economic concerns, and more successful at eliciting collaboration from the population. Secondly, most countries led by women are also those with a stronger focus on social equality, human needs and generosity. These societies are more receptive to political agendas that place social and environmental wellbeing at the core of national policymaking.

12: Markedly heterogeneous COVID-19 testing plans among US colleges and universities
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Posted 11 Aug 2020

Markedly heterogeneous COVID-19 testing plans among US colleges and universities
6,322 downloads medRxiv health policy

A. Sina Booeshaghi, Fayth Hui Tan, Benjamin Renton, Zackary Berger, Lior Pachter

As the COVID-19 pandemic worsens in the United States, colleges that have invited students back for the fall are finalizing mitigation plans to lessen the spread of SARS-CoV-2. Even though students have largely been away from campuses over the summer, several outbreaks associated with colleges have already occurred, foreshadowing the scale of infection that could result from hundreds of thousands of students returning to college towns and cities. While many institutions have released return-to-campus plans designed to reduce viral spread and to rapidly identify outbreaks should they occur, in many cases communications by college administrators have been opaque. To contribute to an evaluation of university preparedness for the COVID-19 pandemic, we assessed a crucial element: COVID-19 on-campus testing. We examined testing plans at more than 500 colleges and universities throughout the US, and collated statistics, as well as narratives from publicly facing websites. We discovered a highly variable and muddled state of COVID-19 testing plans among US institutions of higher education that has been shaped by discrepancies between scientific studies and federal guidelines. We highlight cases of divergence between university testing plans and public health best practices, as well as potential bioethical issues.

13: The Immediate Effect of COVID-19 Policies on Social Distancing Behavior in the United States
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Posted 10 Apr 2020

The Immediate Effect of COVID-19 Policies on Social Distancing Behavior in the United States
4,566 downloads medRxiv health policy

Rahi Abouk, Babak Heydari

Anecdotal evidence points to the effectiveness of COVID-19 social distancing policies, however, their effectiveness vis-a-vis what is driven by public awareness and voluntary actions have not been studied. Policy variations across US states create a natural experiment to study the causal impact of each policy. Using a difference-in-differences methodology, location-based mobility, and daily state-level data on COVID-19 tests and confirmed cases, we rank policies based on their effectiveness. We show that statewide stay-at-home orders had the strongest causal impact on reducing social interactions. In contrast, most of the expected impact of more lenient policies were already reaped from non-policy mechanisms. Moreover, stay-at-home policy results in a steady decline in confirmed cases, starting from ten days after implementation and reaching a 37% decrease after fifteen days, consistent with the testing practices and incubation period of the disease.

14: A Model Based Analysis for COVID-19 Pandemic in India: Implications for Health Systems and Policy for Low- and Middle-Income Countries
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Posted 12 Jun 2020

A Model Based Analysis for COVID-19 Pandemic in India: Implications for Health Systems and Policy for Low- and Middle-Income Countries
4,342 downloads medRxiv health policy

Shankar Prinja, Pankaj Bahuguna, Yashika Chugh, Anna Vassal, Arvind Pandey, Sumit Aggarwal, Narendra Kumar Arora

The authors have withdrawn this manuscript because they do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

15: Global Assessment of the Relationship between Government Response Measures and COVID-19 Deaths
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Posted 06 Jul 2020

Global Assessment of the Relationship between Government Response Measures and COVID-19 Deaths
3,927 downloads medRxiv health policy

Thomas Hale, Andrew J Hale, Beatriz Kira, Anna Petherick, Toby Phillips, Devi Sridhar, Robin Thompson, Samuel Webster, Noam Angrist

Objective: To provide an early global assessment of the impact of government stringency measures on the rate of growth in deaths from COVID-19. We hypothesized that the overall stringency of a government's interventions and the speed of implementation would affect the growth and level of deaths related to COVID-19 in that country. Design: Observational study based on an original database of global governmental responses to the COVID-19 pandemic. Daily data was collected on a range of containment and closure policies for 170 countries from January 1, 2020 until May 27, 2020 by a team of researchers at Oxford University, UK. These data were combined into an aggregate stringency index (SI) score for each country on each day (range: 0-100). Regression was used to show correlations between the speed and strength of government stringency and deaths related to COVID-19 with a number of controls for time and country-specific demographic, health system, and economic characteristics. Interventions: Nine non-pharmaceutical interventions such as school and work closures, restrictions on international and domestic travel, public gathering bans, public information campaigns, as well as testing and contact tracing policies. Main outcomes measures: The primary outcome was deaths related to COVID-19, measured both in terms of maximum daily deaths and growth rate of daily deaths. Results: For each day of delay to reach an SI 40, the average daily growth rate in deaths was 0.087 percentage points higher (0.056 to 0.118, P<0.001). In turn, each additional point on the SI was associated with a 0.080 percentage point lower average daily growth rate (-0.121 to -0.039, P<.001). These daily differences in growth rates lead to large cumulative differences in total deaths. For example, a week delay in enacting policy measures to SI 40 would lead to 1.7 times as many deaths overall. Conclusions: A lower degree of government stringency and slower response times were associated with more deaths from COVID-19. These findings highlight the importance of non-pharmaceutical responses to COVID-19 as more robust testing, treatment, and vaccination measures are developed.

16: COVID-19 pandemic in the African continent: forecasts of cumulative cases, new infections, and mortality
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Posted 14 Apr 2020

COVID-19 pandemic in the African continent: forecasts of cumulative cases, new infections, and mortality
3,821 downloads medRxiv health policy

Tom Achoki, Uzma Alam, Lawrence Were, Tesfaye Gebremedhin, Flavia Senkubuge, Abaleng Lesego, Shuangzhe Liu, Richard Wamai, Yohannes Kinfu

Background: The epidemiology of COVID-19 remains speculative in Africa. To the best of our knowledge, no study, using robust methodology provides its trajectory for the region or accounts for the local context. This paper is the first systematic attempt to provide prevalence, incidence, and mortality estimates across Africa. Methods: Caseloads and incidence forecasts are from a co-variate-based instrumental variable regression model. Fatality rates from Italy and China were applied to generate mortality estimates after making relevant health system and population-level characteristics related adjustments between each of the African countries. Results: By June 30 2020, around 16.3 million people in Africa will contract COVID-19 (95% CI 718,403 to 98,358,799). Northern and Eastern Africa will be the most and least affected areas. Cumulative cases by June 30 are expected to reach around 2.9 million (95% CI 465,028 to 18,286,358) in Southern Africa, 2.8 million (95% CI 517,489 to 15,056,314) in Western Africa, and 1.2 million (95% CI 229,111 to 6,138,692) in Central Africa. Incidence for the month of April 2020 is expected to be highest in Djibouti, 32.8 per 1000 (95% CI 6.25 to 171.77), while Morocco will experience among the highest fatalities (1,045 deaths, 95% CI 167 to 6,547). Conclusion: Less urbanized countries with low levels of socio-economic development (hence least connected to the world) are likely to register lower and slower transmissions at the early stages of an epidemic. However, the same enabling factors that worked for their benefit can hinder interventions that have lessened the impact of COVID- 19 elsewhere.

17: Corona Epidemic in Indian context: Predictive Mathematical Modelling
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Posted 07 Apr 2020

Corona Epidemic in Indian context: Predictive Mathematical Modelling
3,686 downloads medRxiv health policy

Jyoti Bhola, Vandana Revathi Venkateswaran, Monika Koul

The novel Coronavirus pathogen Covid-19 is a cause of concern across the world as the human-to-human infection caused by it is spreading at a fast pace. The virus that first manifested in Wuhan, China has travelled across continents. The increase in number of deaths in Italy, Iran, USA, and other countries has alarmed both the developed and developing countries. Scientists are working hard to develop a vaccine against the virus, but until now no breakthrough has been achieved. India, the second most populated country in the world, is working hard in all dimensions to stop the spread of community infection. Health care facilities are being updated; medical and paramedical staffs are getting trained, and many agencies are raising awareness on the issues related to this virus and its transmission. The administration is leaving no stone unturned to prepare the country to mitigate the adverse effects. However, as the number of infected patients, and those getting cured is changing differently in different states everyday it is difficult to predict the spread of the virus and its fate in Indian context. Different states have adopted measures to stop the community spread. Considering the vast size of the country, the population size and other socio-economic conditions of the states, a single uniform policy may not work to contain the disease. In this paper, we discuss a predictive mathematical model that can give us some idea of the fate of the virus, an indicative data and future projections to understand the further course this pandemic can take. The data can be used by the health care agencies, the Government Organizations and the Planning Commission to make suitable arrangements to fight the pandemic. Though the model is preliminary, it can be used at regional level to manage the health care system in the present scenario. The recommendations can be made, and advisories prepared based on the predictive results that can be implemented at regional levels.

18: The impact of lockdown measures on COVID-19: a worldwide comparison
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Posted 26 May 2020

The impact of lockdown measures on COVID-19: a worldwide comparison
3,433 downloads medRxiv health policy

Dimitris I Papadopoulos, Ivo Donkov, Konstantinos Charitopoulos, Samuel Bishara

Objective We aimed to determine which aspects of the COVID-19 national response are independent predictors of COVID-19 mortality and case numbers. Design Comparative observational study between nations using publically available data Setting Worldwide Participants Covid-19 patients Interventions Stringency of 11 lockdown policies recorded by the Blavatnik School of Government database and earliness of each policy relative to first recorded national cases Main outcome measures Association with log10 National deaths (LogD) and log10 National cases (LogC) on the 29th April 2020 corrected for predictive demographic variables Results Early introduction was associated with reduced mortality (n=137) and case numbers (n=150) for every policy aside from testing policy, contact tracing and workplace closure. Maximum policy stringency was only found to be associated with reduced mortality (p=0.003) or case numbers (p=0.010) for international travel restrictions. A multivariate model, generated using demographic parameters (r2=0.72 for LogD and r2=0.74 for LogC), was used to assess the timing of each policy. Early introduction of first measure (significance p=0.048, regression coefficient {beta}=-0.004, 95% confidence interval 0 to -0.008), early international travel restrictions (p=0.042, ({beta}=-0.005, -0.001 to -0.009) and early public information (p=0.021, {beta}=-0.005, -0.001 to -0.009) were associated with reduced LogC. Early introduction of first measure (p=0.003, {beta}=-0.007, -0.003 to -0.011), early international travel restrictions (p=0.003, {beta}=-0.008, -0.004 to-0.012), early public information (p=0.003, {beta}=-0.007, 0.003 to -0.011), early generalised workplace closure (p=0.031, {beta}=-0.012, -0.002 to -0.022) and early generalised school closure (p=0.050, {beta}=-0.012, 0 to -0.024) were associated with reduced LogC. Conclusions At this stage in the pandemic, early institution of public information, international travel restrictions, and workplace closure are associated with reduced COVID-19 mortality and maintaining these policies may help control the pandemic.

19: Impact of policy interventions and social distancing on SARS-CoV-2 transmission in the United States
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Posted 06 May 2020

Impact of policy interventions and social distancing on SARS-CoV-2 transmission in the United States
3,118 downloads medRxiv health policy

Nickolas Dreher, Zachary Spiera, Fiona M McAuley, Lindsey Kuohn, John R Durbin, Naoum Fares Marayati, Muhammad Ali, Adam Y Li, Theodore C Hannah, Alex Gometz, JT Kostman, Tanvir F Choudhri

Background: Policymakers have employed various non-pharmaceutical interventions (NPIs) such as stay-at-home orders and school closures to limit the spread of Coronavirus disease (COVID-19). However, these measures are not without cost, and careful analysis is critical to quantify their impact on disease spread and guide future initiatives. This study aims to measure the impact of NPIs on the effective reproductive number (Rt) and other COVID-19 outcomes in U.S. states. Methods: In order to standardize the stage of disease spread in each state, this study analyzes the weeks immediately after each state reached 500 cases. The primary outcomes were average Rt in the week following 500 cases and doubling time from 500 to 1000 cases. Linear and logistic regressions were performed in R to assess the impact of various NPIs while controlling for population density, GDP, and certain health metrics. This analysis was repeated for deaths with doubling time from 50 to 100 deaths and included several healthcare infrastructure control variables. Results: States that had a stay-at-home order in place at the time of their 500th case are associated with lower average Rt the following week compared to states without a stay-at-home order (p < 0.001) and are significantly less likely to have an Rt>1 (OR 0.07, 95% CI 0.01 to 0.37, p = 0.004). These states also experienced a significantly longer doubling time from 500 to 1000 cases (HR 0.35, 95% CI 0.17 to 0.72, p = 0.004). States in the highest quartile of average time spent at home were also slower to reach 1000 cases than those in the lowest quartile (HR 0.18, 95% CI 0.06 to 0.53, p = 0.002). Discussion: Few studies have analyzed the effect of statewide stay-at-home orders, school closures, and other social distancing measures in the U.S., which has faced the largest COVID-19 case burden. States with stay-at-home orders have a 93% decrease in the odds of having a positive Rt at a standardized point in disease burden. States that plan to scale back such measures should carefully monitor transmission metrics. Key words: COVID-19, SARS-CoV-2, Coronavirus, Public Policy, Social Distancing, Non-pharmaceutical Interventions, Stay-at-home Order, Shelter-in-place.

20: World governments should protect their population from COVID-19 pandemic using Italy and Lombardy as precursor
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Posted 27 Mar 2020

World governments should protect their population from COVID-19 pandemic using Italy and Lombardy as precursor
3,038 downloads medRxiv health policy

M. Supino, A. d’Onofrio, F. Luongo, G. Occhipinti, A. Dal Co

The COVID-19 pandemic is spreading worldwide. Italy emerged early on as the country with the largest outbreak outside Asia. The outbreak in Northern Italy demonstrates that it is fundamental to contain the virus spread at a very early stage of diffusion. At later stages, no containment measure, even if strict, can prevent the saturation of the hospitals and of the intensive care units in any country. Here we show that it is possible to predict when the intensive care units will saturate, within a few days from the first cases of COVID-19 intensive care patients. Using early counts of intensive care patients, we predict the saturation for Lombardy, Italy. Governments should use the Italian precursor to control the outbreak of COVID-19 and prevent the saturation of their intensive care units to protect their population.

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