Most downloaded biology preprints, all time
in category health policy
399 results found. For more information, click each entry to expand.
1,854 downloads medRxiv health policy
The outbreak of COVID-19 has spurred extensive research worldwide to develop a vaccine. However, when a vaccine becomes available, limited production and distribution capabilities will likely lead to another challenge: who to prioritize for vaccination to mitigate the near-end impact of the pandemic? To tackle that question, this paper first expands a state-of-the-art epidemiological model, called DELPHI, to capture the effects of vaccinations and the variability in mortality rates across subpopulations. It then integrates this predictive model into a prescriptive model to optimize vaccine allocation, formulated as a bilinear, non-convex optimization model. To solve it, this paper proposes a coordinate descent algorithm that iterates between optimizing vaccine allocations and simulating the dynamics of the pandemic. We implement the model and algorithm using real-world data in the United States. All else equal, the optimized vaccine allocation prioritizes states with a large number of projected cases and sub-populations facing higher risks (e.g., older ones). Ultimately, the optimized vaccine allocation can reduce the death toll of the pandemic by an estimated 10-25%, or 10,000-20,000 deaths over a three-month period in the United States alone. Highlights- This paper formulates an optimization model for vaccine allocation in response to the COVID-19 pandemic. This model, referred to as DELPHI-V-OPT, integrates a predictive epidemiological model into a prescriptive model to support the allocation of vaccines across geographic regions (e.g., US states) and across risk classes (e.g., age groups). - This paper develops a scalable coordinate descent algorithm to solve the DELPHI-V-OPT model. The proposed algorithm converges effectively and in short computational times. Therefore, the proposed approach can be implemented efficiently, and allows extensive sensitivity analyses for scenario planning and policy analysis. - Computational results demonstrate that optimized vaccine allocation strategies can curb the death toll of the COVID-19 pandemic by an estimated at 10-25%, or 10,000-20,000 deaths over a three-month period in the United States alone. These results highlight the critical role of vaccine allocation to combat the COVID-19 pandemic, in addition to vaccine design and vaccine production.
1,723 downloads medRxiv health policy
To reliably estimate the demand on regional health systems and perform public health planning, it is necessary to have a good estimate of the prevalence of infection with SARS-CoV-2 (the virus that causes COVID-19) in the population. In the absence of wide-spread testing, we provide one approach to infer prevalence based on the assumption that the fraction of true infections needing hospitalization is fixed and that all hospitalized cases of COVID-19 in Santa Clara are identified. Our goal is to estimate the prevalence of SARS-CoV-2 infections, i.e. the true number of people currently infected with the virus, divided by the total population size. Our analysis suggests that as of March 17, 2020, there are 6,500 infections (0.34% of the population) of SARS-CoV-2 in Santa Clara County. Based on adjusting the parameters of our model to be optimistic (respectively pessimistic), the number of infections would be 1,400 (resp. 26,000), corresponding to a prevalence of 0.08% (resp. 1.36%). If the shelter-in-place led to R0 < 1, we would expect the number of infections to remain about constant for the next few weeks. However, even if this were true, we expect to continue to see an increase in hospitalized cases of COVID-19 in the short term due to the fact that infection of SARS-CoV-2 on March 17th can lead to hospitalizations up to 14 days later.
1,700 downloads medRxiv health policy
Public mask use has emerged as a key tool in response to COVID-19. We develop and document a classification of statewide mask mandates that reveals variation in their scope and timing. Some U.S. states quickly mandated the wearing of face coverings in most public spaces, whereas others issued narrow mandates or no mandate at all. We consider how differences in COVID-19 epidemiological indicators and partisan politics affect when states adopted broad mask mandates, starting with the earliest broad public mask mandates in April 2020 and continuing though the end of 2020. The most important predictor is whether a state is led by a Republican governor. These states adopt statewide indoor mask mandates an estimated 98.0 days slower (95% CI: 88.8 to 107.3), if they did so at all (hazard ratio of 7.54, 95% CI: 2.87 to 16.19). COVID-19 indicators such as confirmed cases or deaths per million are much less important predictors of statewide mask mandates. This finding highlights a key challenge to public efforts to increase mask-wearing, one of the most effective tools for preventing the spread of SARS-CoV-2 while restoring economic activity.
1,611 downloads medRxiv health policy
Background. The detection of SARS-CoV-2 RNA by real-time polymerase chain reaction (PCR) in respiratory samples from COVID-19 patients is not a direct indication of the presence of viable viruses. The isolation of SARS-CoV-2 in cell culture system however, can acts as surrogate marker of infectiousness. Cell culture based studies performed mostly with hospitalized and moderate/severe COVID-19 claims that no replication competent virus is found after 9 days of the symptoms onset in respiratory samples. Therefore, it is now recommended 10 days isolation before patient discharge. Methods. We cell-cultured 29 SARS-COV-2 RT-PCR positive respiratory samples at the 10th day after the illness in Vero E6 cells. After two passages, cytopathic effect and cycle threshold (CT) lower than the obtained in the original sample were used to determine positivity. Findings. We found viable particles in (7/29) 24% of samples tested. The positivity in cell culture was strongly associated (p<0.0001) to the low cycle thresholds in clinical samples (Ct <21). Conclusion. This data adds important knowledge to the current protocols for de-isolation of patients with non-hospitalized mild COVID-19.
1,604 downloads medRxiv health policy
Background. A great concern around the globe now is to mitigate the COVID-19 pandemic via contact tracing. Analyzing the control strategies during the first five months of 2020 in Singapore is important to estimate the effectiveness of contacting tracing measures. Methods. We developed a mathematical model to simulate the COVID-19 epidemic in Singapore, with local cases stratified into 5 categories according to the conditions of contact tracing and self-awareness. Key parameters of each category were estimated from local surveillance data. We also simulated a set of possible scenarios to predict the effects of contact tracing and self-awareness for the following month. Findings. During January 23 - March 16, 2020, the success probabilities of contact tracing and self-awareness were estimated to be 31% (95% CI 28%-33%) and 54% (95% CI 51%-57%), respectively. During March 17 - April 7, 2020, several social distancing measures (e.g., limiting mass gathering) were introduced in Singapore, which, however, were estimated with minor contribution to reduce the non-tracing reproduction number per local case (R_(l,2)). If contact tracing and self-awareness cannot be further improved, we predict that the COVID-19 epidemic will continue to spread in Singapore if R_(l,2)[≥]1.5. Conclusion. Contact tracing and self-awareness can mitigate the COVID-19 transmission, and can be one of the key strategies to ensure a sustainable reopening after lifting the lockdown.
1,575 downloads medRxiv health policy
Background: Timely and effective contact tracing is an essential public health role to curb the transmission of COVID-19. App-based contact tracing has the potential to optimise the resources of overstretched public health departments. However, its efficiency is dependent on wide-spread adoption. We aimed to identify the proportion of people who had downloaded the Australian Government COVIDSafe app and examine the reasons why some did not. Method: An online national survey with representative quotas for age and gender was conducted between May 8 and May 11 2020. Participants were excluded if they were a healthcare professional or had been tested for COVID-19. Results: Of the 1802 potential participants contacted, 289 were excluded, 13 declined, and 1500 participated in the survey (response rate 83%). Of survey participants, 37% had downloaded the COVIDSafe app, 19% intended to, 28% refused, and 16% were undecided. Equally proportioned reasons for not downloading the app included privacy (25%) and technical concerns (24%). Other reasons included a belief that social distancing was sufficient and the app is unnecessary (16%), distrust in the Government (11%), and apathy (11%). In addition, COVIDSafe knowledge varied with confusion about its purpose and capabilities. Conclusion: For the COVIDSafe app to be accepted by the public and used correctly, public health messages need to address the concerns of its citizens, specifically in regards to privacy, data storage, and technical capabilities. Understanding the specific barriers preventing the uptake of tracing apps provides the opportunity to design targeted communication strategies aimed at strengthening public health initiatives such as download and correct use.
1,559 downloads medRxiv health policy
The U.S. is the epicenter of the coronavirus disease 2019 (COVID-19) pandemic. In response, governments have implemented measures to slow transmission through "social distancing." However, the practice of social distancing may depend on prevailing socioeconomic conditions and beliefs. Using 15-17 million anonymized cell phone records, we find that lower per capita income and greater Republican orientation were associated with significantly reduced social distancing among U.S. counties. These associations persisted after adjusting for county-level sociodemographic and labor market characteristics as well as state fixed effects. These results may help policymakers and health professionals identify communities that are most vulnerable to transmission and direct resources and communications accordingly.
1,463 downloads medRxiv health policy
Amélie Desvars-Larrive, Elma Dervic, Nils Haug, Thomas Niederkrotenthaler, Jiaying Chen, Anna Di Natale, Jana Lasser, Diana S Gliga, Alexandra Roux, Abhijit Chakraborty, Alexandr Ten, Alija Dervic, Andrea Pacheco, David Cserjan, Diana Lederhilger, Dorontine Berishaj, Erwin Flores Tames, Huda Takriti, Jan Korbel, Jenny Reddish, Johannes Stangl, Lamija Hadziavdic, Laura Stoeger, Leana Gooriah, Lukas Geyrhofer, Marcia R Ferreira, Rainer Vierlinger, Samantha Holder, Samuel Alvarez, Simon Haberfellner, Verena Ahne, Viktoria Reisch, Vito DP Servedio, Xiao Chen, Xochilt Maria Pocasangre-Orellana, David Garcia, Stefan Thurner
In response to the COVID-19 pandemic, governments have implemented a wide range of nonpharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset on a weekly basis until the end of December 2020.
1,450 downloads medRxiv health policy
Objectives To conduct a rapid review on the efficacy and policy of contact tracing, testing, and isolation (TTI) in Covid-19 prevention and control, including a case study for their delivery. Method Research articles and reviews on the use of contact tracing, testing, self-isolation and quarantine for Covid-19 management published in English within 1 year (2019 to 28th May, 2020) were eligible to the review. We searched MEDLINE (PubMed), Cochrane Library, SCOPUS and JSTOR with search terms included "contact tracing" or "testing" or "self-isolation" or "quarantine" in the title in combination with "Covid-19" or "COVID-19" or "coronavirus" in the title or abstract. Studies not associated with TTI or Covid-19 or being solely commentary were excluded. A narrative synthesis with a tabulation system was used to analyse studies for their diverse research designs, methods, and implications. Information for the case study was obtained from the Centers for Disease Control Taiwan. Results Among the 160 initial publications, 30 eligible studies are included in the review. Included studies applied various designs: experiments, clinical studies, Government Documents, systematic reviews, observational studies, surveys, practice guidelines, technical reports. A case study on TTI delivery is summarised based on policy and procedures in Taiwan. Conclusions The information included in the review may inform the TTI program in the UK.
1,359 downloads medRxiv health policy
Social distancing is a central public health measure in the fight against the COVID-19 pandemic, but individuals' compliance cannot be taken for granted. We use a survey experiment to examine the prevalence of non-compliance with social distancing in nine countries and test pre-registered hypotheses about individual-level characteristics associated with less social distancing. Leveraging a list experiment to control for social desirability bias, we find large cross-national variation in adherence to social distancing guidelines. Compliance varies systematically with COVID-19 fatalities and the strictness of lockdown measures. We also find substantial heterogeneity in the role of individual-level predictors. While there is an ideological gap in social distancing in the US and New Zealand, this is not the case in European countries. Taken together, our results suggest caution when trying to model pandemic health policies on other countries' experiences. Behavioral interventions targeted towards specific demographics that work in one context might fail in another.
1,353 downloads medRxiv health policy
Based on harmonized census data from 81 countries, we estimate how age and co-residence patterns shape the vulnerability of countries' populations to outbreaks of covid-19. We estimate variation in deaths arising due to a simulated random infection of 10% of the population living in private households and subsequent within-household transmission of the virus. The age-structures of European and North American countries increase their vulnerability to covid-related deaths in general. The co-residence patterns of elderly persons in Africa and parts of Asia increase these countries' vulnerability to deaths induced by within-household transmission of covid-19. Southern European countries, which have aged populations and relatively high levels of intergenerational co-residence are, all else equal, the most vulnerable to outbreaks of covid-19. In a second step, we estimate to what extent avoiding primary infections for specific age-groups would prevent subsequent deaths due to within-household transmission of the virus. Preventing primary infections among the elderly is the most effective in countries with small households and little intergenerational co-residence such as France, whereas confining younger age groups can have a greater impact in countries with large and inter-generational households such as Bangladesh.
1,304 downloads medRxiv health policy
Objectives: Norway and Sweden, two neighboring countries with similar populations, health care systems and socioeconomics, have reacted differently to the COVID-19 pandemic. Norway closed all kindergartens, schools and universities, and banned sports and cultural activities, while Sweden kept most institutions and trainings facilities open. We aimed to compare peoples' attitudes towards authorities and control measures, and effects on life in Norway and Sweden. Design: Anonymous web-based surveys for individuals age 15 or older distributed through Facebook using the snowball method. Setting: Norway and Sweden, mid-March to mid-April, 2020. Participants: Altogether, 3,508 individuals participated in the survey; 3000 in Norway and 508 in Sweden. 79% of the participants were women, 60% of the Norwegians and 47% of the Swedes were between 30-49 years, and around 45% of the participants in both countries had more than 4 years of higher education. Outcome measures: Perceived threat of the pandemic, views on infection control measures, and impact on daily life. We performed descriptive analyses of the responses and compared the two countries. Results: People had high trust in the health services in both countries, but differed in the degree of trust in their government (17% had high trust in Norway and 37% in Sweden). More Norwegians than Swedes agreed that school closure was a good measure (66% Norway and 18% in Sweden), and that countries with open schools were irresponsible (65% in Norway and 23% in Sweden). About the same amount responded that COVID-19 was a large to very large threat to the population (53% in Norway and 58% in Sweden), whereas more Norwegians than Swedes responded that the threat from repercussions of the mitigation measures were large or very large (71% in Norway and 56% in Sweden). Compliance with infection preventive measures was high and similar in the two countries (more than 98%). In Norway, 69% lived a more sedentary life during the pandemic versus 50% in Sweden; and Norwegians reported they ate more than Swedes (44% in Norway and 33% in Sweden). Conclusion: Sweden, with less restrictive measures against the COVID-19 pandemic, had a higher level of trust in the authorities, while Norwegians reported a more negative lifestyle during the pandemic. The level of trust in the health care system and self-reported compliance with preventive measures was high in both countries.
1,303 downloads medRxiv health policy
Daniel C. P. Jorge, Moreno S. Rodrigues, Mateus S. Silva, Luciana L. Cardim, Nivea B. da Silva, Ismael H. Silveira, Vivian A. F. Silva, Felipe A. C. Pereira, Arthur R. de Azevedo, Alan A. S. Amad, Suani T. R. Pinho, Roberto F. S. Andrade, Pablo I. P. Ramos, Juliane Fonseca Oliveira
COVID-19 is now identified in almost all countries in the world, with poorer regions being particularly more disadvantaged to efficiently mitigate the impacts of the pandemic. In the absence of efficient therapeutics or vaccines, control strategies are currently based on non-pharmaceutical interventions, comprising changes in population behavior and governmental interventions, among which the prohibition of mass gatherings, closure of non-essential establishments, quarantine and movement restrictions. In this work we analyzed the effects of 707 published governmental interventions, and population adherence thereof, on the dynamics of COVID-19 cases across all 27 Brazilian states, with emphasis on state capitals and remaining inland cities. A generalized SEIR (Susceptible, Exposed, Infected and Removed) model with a time-varying transmission rate (TR), that considers transmission by asymptomatic individuals, is presented. We analyze the effect of both the extent of enforced measures across Brazilian states and population movement on the changes in the TR and effective reproduction number. The social mobility reduction index, a measure of population movement, together with the stringency index, adapted to incorporate the degree of restrictions imposed by governmental regulations, were used in conjunction to quantify and compare the effects of varying degrees of policy strictness across Brazilian states. Our results show that population adherence to social distance recommendations plays an important role for the effectiveness of interventions and represents a major challenge to the control of COVID-19 in low- and middle-income countries.
1,293 downloads medRxiv health policy
The number of confirmed COVID-19 cases, relative to population size, has varied greatly throughout the United States and even within the same city. In different zip codes in New York City, the epicentre of the epidemic, the number of cases per 100,000 residents has ranged from 437 to 4227, a 1:10 ratio. To guide policy decisions regarding containment and reopening of the economy, schools, and other institutions, it is vital to identify the factors that drive this large variation. This paper reports on a statistical study of incidence variation by zip code across New York City. Among many socio-economic and demographic measures considered, the average household size emerges as the single most important explanatory variable: an increase in average household size by one member increases the zip code incidence rate, in our final model specification, by at least 876 cases, 23% of the range of incidence rates, at a 95% confidence level. The percentage of the population above the age of 65, the percentage below the poverty line, and their interaction term are also strongly positively associated with zip code incidence rates, In terms of ethnic/racial characteristics, the percentages of African Americans, Hispanics, and Asians within the population, are significantly associated, but the magnitude of the impact is considerably smaller. (The proportion of Asians within a zip code has a negative association.) These significant associations may be explained by comorbidities, known to be more (less) prevalent among the black and Hispanic (Asian) population segments. In turn, the increased prevalence of these comorbidities among the black and Hispanic population, is, in large part, the result of poorer dietary habits and more limited access to healthcare, themselves driven by lower incomes Contrary to popular belief, population density, per se, does not have a significantly positive impact. Indeed, population density and zip code incidence rates are negatively correlated, with a -33% correlation coefficient. Our model specification is based on a well-established epidemiologic model that explains the effects of household sizes on R0, the basic reproductive number of an epidemic. Our findings support implemented and proposed policies to quarantine pre-acute and post-acute patients, as well as nursing home admission policies.
1,292 downloads medRxiv health policy
The COVID-19 outbreak highlights the vulnerability to novel infections, and vaccination remains a foreseeable method to return to normal life. However, infrastructure is inadequate for the whole population to be vaccinated immediately. Therefore, policies have adopted a strategy to vaccinate the elderly and vulnerable population while delaying others. This study uses the Tennessee official statistic from the onset of COVID vaccination (17th of December 2021) to understand how age-specific vaccination strategies reduce daily cases, hospitalization, and death rate. The result shows that vaccination strategy can significantly influence the numbers of patients with COVID-19 in all age groups and lower hospitalization and death rates just in older age groups. The Elderly had a 95% lower death rate from December to March; however, and no change in the death rate in other age groups. The Hospitalization rate was reduced by 80% in this study cohort for people aged 80 or older, while people who were between 50 to 70 had almost the same hospitalization rate. The study indicates that vaccination targeting older age groups is the optimal way to avoid higher transmissions and reduce hospitalization and death rate for older groups.
1,288 downloads medRxiv health policy
Objectives: To estimate the impact of various mitigation strategies on COVID-19 transmission in a U.S. jail beyond those offered in national guidelines. Methods: We developed a stochastic dynamic transmission model of COVID-19 in one large urban U.S. jail among staff and incarcerated individuals. We divided the outbreak into four intervention phases: the start of the outbreak, depopulation of the jail, increased proportion of people in single cells, and asymptomatic testing. We used the next generation method to estimate the basic reproduction ratio, R0, in each phase. We estimated the fraction of new cases, hospitalizations, and deaths averted by these interventions along with the standard measures of sanitization, masking, and social distancing interventions. Results: For the first outbreak phase, the estimated R0 was 8.23 (95% CrI: 5.01-12.90), and for the subsequent phases, R0, phase 2 = 3.58 (95% CrI: 2.46-5.08), R0, phase 3 = 1.72 (95% CrI: 1.41-2.12), and R0, phase 4 = 0.45 (95% CrI: 0.32-0.59). In total, the jail's interventions prevented approximately 83% of projected cases and hospitalizations and 89% of deaths over 83 days. Conclusions: Depopulation, single celling, and asymptomatic testing within jails can be effective strategies to mitigate COVID-19 transmission in addition to standard public health measures. Policy Implications: Decision-makers should prioritize reductions in the jail population, single celling, and testing asymptomatic populations, as additional measures to manage COVID-19 within correctional settings.
1,287 downloads medRxiv health policy
Background: There is a lack of empirical evidence that lockdowns decrease daily cases of COVID-19 and related mortality compared to herd immunity. England implemented a delayed lockdown on March 23, 2020, but Sweden did not. We aim to examine the effect of lockdown on daily COVID-19 cases and related deaths during the first 100 days post-lockdown. Methods: We compared daily cases of COVID-19 infection and related mortality in England and Sweden before and after lockdown intervention using a comparative-interrupted time series analysis. The period included was from COVID-19 pandemic onset till June 30, 2020. Results: The adjusted-rate of daily COVID-19 infections was eight cases/10,000,000 person higher in England than Sweden before lockdown order (95% CI: 2-14, P=0.01). On the day of intervention (lagged lockdown), England had 693 more COVID-19 cases/10,000,000 person compared to Sweden (95% CI: 467-920, P<0.001). Compared to the pre-intervention period, the adjusted daily confirmed cases rate decreased by 19 cases/ 10,000,000 person compared to Sweden (95% CI: 13-26, P<0.001). There was a rate excess of 1.5 daily deaths/ 10,000,000 person in England compared to Sweden pre-intervention (95% CI: 1-2, P<0.001). The increased mortality rate resulted in 50 excess deaths/ 10,000,000 person related to COVID-19 in England compared to Sweden on the day of lockdown (95% CI: 30-71, P<0.001). Post-intervention, the rate of daily deaths in England decreased by two deaths/ 10,000,000 person compared to Sweden (95% CI: 1-3, P<0.001). During phases one and two of lockdown lifting in England, there was no rebound increase in daily cases or deaths compared to Sweden. Conclusion: The lockdown order implemented in England on March 23, 2020, effectively decreased the daily new cases rate and related mortality compared to Sweden. There was no short-term increase in COVID-19 cases and related-deaths after the phases one and two of the lifting of restrictions in England compared to Sweden. This study provides empirical, comparative evidence that lockdowns slow the spread of COVID-19 in communities compared to herd immunity.
1,249 downloads medRxiv health policy
Importance: In recent years, the US Food and Drug Administration (FDA) and manufacturers have increasingly sought to expedite patient access to first-of-a-kind devices via the De Novo premarket review pathway. Understanding the strength of clinical evidence supporting FDA clearance through this pathway can help guide clinical adoption of novel devices and ongoing regulatory development of the postmarket surveillance infrastructure. Objective: Our primary objective was to characterize the strength of clinical evidence supporting FDA clearance of therapeutic De Novo devices. Key secondary objectives were 1) characterization of FDA post-marketing requirements for De Novo devices and 2) study of the use of these devices as the basis for devices subsequently cleared via the 510(k) process. Design: Retrospective cross-sectional analysis Setting: Publicly available online FDA databases, including the De Novo database, the 510(k) clearance database, the 522 Post Market Surveillance database, and the Recalls of Medical Devices database Participants: All moderate-risk therapeutic devices cleared via the De Novo pathway between January 1, 2011, and December 31, 2019. Main Outcome Measures: (1) proportion of De Novo devices cleared based on evidence from a pivotal clinical study, (2) proportion of pivotal study primary effectiveness endpoints that were met, (3) proportion of De Novo devices subject to FDA-required postmarket studies, and (4) proportion of De Novo devices serving as the basis for at least one subsequently cleared 510(k) device (i.e., new models or competitor products). Results: There were 63 (of 65; 96.9%) moderate-risk therapeutic devices cleared by FDA via the De Novo pathway between 2011 and 2019 for which decision summary documentation was publicly available. Of the 63 devices, 51 (81.0%) were supported by pivotal clinical studies (n=54 studies); the remainder (n=12; 19.0%) were not supported by a pivotal clinical study. The majority of pivotal studies were randomized (57.4%), multi-armed (61.1%), and used an active (25.9%) or sham (35.2%) comparator arm; 17 (31.5%) failed to meet at least one primary effectiveness endpoint. Among the 63 devices cleared via the De Novo pathway, one (1.6%) was subject to an FDA-required posttmarket study and 32 (47.8%) served as a predicate device for new models or competitor devices subsequently cleared through the 510(k) process. Conclusions: Between 2011 and 2019, the FDA cleared the majority of first-of-a-kind moderate-risk therapeutic devices via the De Novo pathway based on premarket evidence from pivotal clinical studies. However, 43% of devices were cleared without clinical evidence from pivotal studies or based on pivotal studies that failed to meet at least one primary effectiveness endpoint. The FDA rarely required postmarket studies of these devices, which often served as the basis for new models and competitor products subsequently cleared via the 510(k) process.
1,249 downloads medRxiv health policy
This study investigated self-policing COVID-19 and civic responsibilities in Lagos Metropolis, Nigeria adopting an online qualitative interview due to the current lockdown that denied field (face to face) interview. Fifty out of the feedbacks from the online interview were picked randomly to arrive at the conclusion of this study. The feedbacks suggested that there is adequate awareness of the COVID-19 pandemic among the people living in Lagos Metropolis, Nigeria and that they are following the directives of federal and state governments in an effort to reduce the community transmission of the infectious diseases. However, the ban on public gatherings and movements has made it impossible for many homes to meet their basic needs especially feeding. The government provided palliatives have also been largely insufficient to cater for the vulnerable. There could be a crisis (such as hunger) and the breakdown of law and order if the government does not increase their capacity to mitigate the hardship which the ongoing lockdown has imposed on the people.
1,227 downloads medRxiv health policy
We present a real-time forecast of COVID-19 in Pakistan that is important for decision-making to control the spread of the pandemic in the country. The study helps to develop an accurate plan to eradicate the COVID-19 by taking calculated steps at the appropriate time, that are crucial in the absence of a tested medicine. We use four phenomenological mathematical models, namely Discrete Exponential Growth model, the Discrete Generalized Growth model, the Discrete Generalized Logistic Growth, and Discrete Generalize Richards Growth model. Our analysis explains the important characteristics quantitatively. The study leads to understand COVID-19 pandemic in Pakistan in three evolutionary stages, and provides understanding to control its spread in the short time domain and in the long term domain. For the reason the study is helpful in devising the measures to handle the emerging threat of similar outbreaks in other countries.
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