Number Of Daily Cases Research Articles

Case growth analysis to inform local response to COVID-19 epidemic in a diverse U.S community

Early detection of new outbreak waves is critical for effective and sustained response to the COVID-19 pandemic. We conducted a growth rate analysis using local community and inpatient records from seven hospital systems to characterize distinct phases in SARS-CoV-2 outbreak waves in the Greater Houston area. We determined the transition times from rapid spread of infection in the community to surge in the number of inpatients in local hospitals. We identified 193,237 residents who tested positive for SARS-CoV-2 via molecular testing from April 8, 2020 to June 30, 2021, and 30,031 residents admitted within local healthcare institutions with a positive SARS-CoV-2 test, including emergency cases. We detected two distinct COVID-19 waves: May 12, 2020–September 6, 2020 and September 27, 2020–May 15, 2021; each encompassed four growth phases: lagging, exponential/rapid growth, deceleration, and stationary/linear. Our findings showed that, during early stages of the pandemic, the surge in the number of daily cases in the community preceded that of inpatients admitted to local hospitals by 12–36 days. Rapid decline in hospitalized cases was an early indicator of transition to deceleration in the community. Our real-time analysis informed local pandemic response in one of the largest U.S. metropolitan areas, providing an operationalized framework to support robust real-world surveillance for outbreak preparedness.

Trends in Psychological Distress and Burnout Syndrome among Healthcare Workers due to COVID-19

The coronavirus COVID-19 pandemic has caused significant physical and mental tension among frontline workers globally. Poor working conditions, lack of protective personal equipment (PPE), short-staffed departments, medication shortage, depleted hospital beds, and ventilators have had a direct correlation with occupational burnout syndrome (BOS) and psychological distress among frontline healthcare workers (HCW) and their physical and mental well-being. The limitless hours on shift, the abundant number of daily cases, and the upturn of fatalities have contributed to the stressors among HCWs during this pandemic. In this paper, we will examine the occupational burnout syndrome and the psychological distress among HCWs working frontline during the pandemic. Also, the paper will explore whether there is a correlation between occupational burnout syndrome, mental and psychological distress, and COVID-19. The goal of this research is to acquire and establish if there had been an increase in anxiety and other mental health concerns as well as burnout levels for workers impacted by COVID-19. Categories explored will entail anxiety levels, mental and physical strains of working long hours, working under subpar conditions with limited PPE and patient rooms, and fear of exposure to the virus. Throughout the pandemic, many cases of tragic suicidal deaths have emerged. Consequently, attention to the well-being of healthcare professionals (HCPs) across the world has become imperative to adequately support and monitor. The Diagnostic and Statistical Manual of Mental Disorders, (DSM-5) and the Maslach Burnout Inventory – Medical Personnel (MBI-MP) are tools used by psychiatrists to diagnose and treat mental health such as burnout syndrome and psychological distress levels which also encompasses post-traumatic stress disorder (PTSD) and mass traumatic events (MTE). Studies have shown a high prevalence of PTSD symptoms, anxiety, fear, depression, and frustration in emergency professionals involved in the COVID-19 pandemic. Through various studies, we will demonstrate how the pandemic has affected frontline workers' mental and psychological well-being as well as how inadequate working conditions and long working hours lead to occupational burnout syndrome. Results will show how healthcare workers are feeling unaccomplished, second-guessing their clinical decisions, defeated, and mentally and physically drained. It is expected that subpar working conditions will continue to deteriorate the physical and mental well-being of HCWs on the frontline as the number of COVID-19 cases continue globally even after three years since its inception.

Investigating the effects of various control measures on economy and spread of COVID-19 in Turkey: a system dynamics approach.

The coronavirus disease 2019 (COVID-19) which began in Wuhan in December 2019 has permeated all over the world in such a short time and was declared as a pandemic by World Health Organization (WHO). The pandemic that is erupting all of a sudden attracts the researchers to examine the spread and effects of the disease as well as the possible treatments and vaccine developments. In addition to the analytical models, such as compartmental modeling, Markov decision process, and so on, simulation and system dynamics (SD) are also widely applied in this field. In this study, we adopt the compartmental modeling stages to build an SD approach for the spread of the disease. A dynamic control measure decision support system (DSS) that varies depending on the number of daily cases is incorporated to the model. Furthermore, the economic loss in the gross domestic product (GDP) and workforce due to hospital stay and death caused by the COVID-19 are also investigated. The model is tested with various numerical parameters and the results are presented. The results on the spread of the disease and the associated economic loss provide meaningful insights into when control measures need to be imposed at which level. We also provide some policy insights, including some alternative policies, such as increasing awareness of people and vaccination in addition to control measures. The results reveal that the total number of cases and deaths is approximately 37% higher in the absence of dynamic DSS. However, everything comes at a price and applying such control measures brings about an increase in the economic loss about 47%.

Early Warning and Monitoring of Coronavirus Disease 2019 Using Baidu Search Index and Baidu Information Index in Guangxi, China

Abstract Coronavirus disease 2019 (COVID-19) is an emerging infectious disease, and it is important to detect early and monitor the disease trend for policymakers to make informed decisions. We explored the predictive utility of Baidu Search Index and Baidu Information Index for early warning of COVID-19 and identified search keywords for further monitoring of epidemic trends in Guangxi. A time-series analysis and Spearman correlation between the daily number of cases and both the Baidu Search Index and Baidu Information Index were performed for seven keywords related to COVID-19 from January 8 to March 9, 2020. The time series showed that the temporal distributions of the search terms “coronavirus,” “pneumonia” and “mask” in the Baidu Search Index were consistent and had 2 to 3 days' lead time to the reported cases; the correlation coefficients were higher than 0.81. The Baidu Search Index volume in 14 prefectures of Guangxi was closely related with the number of reported cases; it was not associated with the local GDP. The Baidu Information Index search terms “coronavirus” and “pneumonia” were used as frequently as 192,405.0 and 110,488.6 per million population, respectively, and they were also significantly associated with the number of reported cases (r s > 0.6), but they fluctuated more than for the Baidu Search Index and had 0 to 14 days' lag time to the reported cases. The Baidu Search Index with search terms “coronavirus,” “pneumonia” and “mask” can be used for early warning and monitoring of the epidemic trend of COVID-19 in Guangxi, with 2 to 3 days' lead time.

Assessment of diagnostic radiographers’ knowledge of work-related health problems and associated effects: A multicentre study in Ghana

IntroductionWork-related health problems (WRHPs) are health conditions peculiar to a group of people or occupations including radiography in a specific work setting. These WRHPs occur as a result of prevailing work conditions which predispose workers to risks of physical or psychological distress. AimThis study assessed the knowledge of WRHPs among practicing radiographers in Ghana and evaluated the sources, causes, effects and preventive measures of WRHPs. MethodsA prospective cross-sectional design incorporating a quantitative data collection approach was used. A questionnaire was used to assess the knowledge and evaluate the effects of WRHPs among two cohorts of 31 practicing radiographers at a regional hospital (RH) and a teaching hospital (TH). ResultsAn average score of 4.2 (SD = 0.4) out of 5 (84.8%) obtained on the knowledge scale indicated very good knowledge of WRHPs among the radiographers. Physical work demands, ergonomic issues, increased workload and stress levels on on-duty radiographers due to sick absence by colleagues, large numbers of daily cases, and extra work without incentives were reported as WRHPs effects mostly experienced by the radiographers. The study also showed no significant difference between gender groups (p = 0.313), years of professional practice experience level (p = 0.319), and academic qualifications (p = 0.287) on knowledge of WRHPs. ConclusionRadiographers working in some referral and teaching hospitals in Ghana demonstrated very good knowledge of WRHPs and identified several effects of WRHPs on professional practice. Implication for practiceThe study concludes that WRHPs predispose radiographers to adverse health conditions, and administrative protocols are required to prevent or mitigate the burden.

Estimation of Excess Deaths Associated With the COVID-19 Pandemic in Istanbul, Turkey.

Background and ObjectivesThe official number of daily cases and deaths are the most prominent indicators used to plan actions against the COVID-19 pandemic but are insufficient to see the real impact. Official numbers vary due to testing policy, reporting methods, etc. Therefore, critical interventions are likely to lose their effectiveness and better-standardized indicators like excess deaths/mortality are needed. In this study, excess deaths in Istanbul were examined and a web-based monitor was developed.MethodsDaily all-cause deaths data between January 1, 2015- November 11, 2021 in Istanbul is used to estimate the excess deaths. Compared to the pre-pandemic period, the % increase in the number of deaths was calculated as the ratio of excess deaths to expected deaths (P-Scores). The ratio of excess deaths to official figures (T) was also examined.ResultsThe total number of official and excess deaths in Istanbul are 24.218 and 37.514, respectively. The ratio of excess deaths to official deaths is 1.55. During the first three death waves, maximum P-Scores were 71.8, 129.0, and 116.3% respectively.ConclusionExcess mortality in Istanbul is close to the peak scores in Europe. 38.47% of total excess deaths could be considered as underreported or indirect deaths. To re-optimize the non-pharmaceutical interventions there is a need to monitor the real impact beyond the official figures. In this study, such a monitoring tool was created for Istanbul. The excess deaths are more reliable than official figures and it can be used as a gold standard to estimate the impact more precisely.

Wastewater monitoring of SARS-CoV-2 shows high correlation with COVID-19 case numbers and allowed early detection of the first confirmed B.1.1.529 infection in Switzerland: results of an observational surveillance study.

Wastewater-based epidemiology has contributed significantly to the comprehension of the dynamics of the current COVID-19 pandemic. Its additional value in monitoring SARS-CoV-2 circulation in the population and identifying newly arising variants independently of diagnostic testing is now undisputed. As a proof of concept, we report here correlations between SARS-CoV-2 detection in wastewater and the officially recorded COVID-19 case numbers, as well as the validity of such surveillance to detect emerging variants, exemplified by the detection of the B.1.1.529 variant Omicron in Basel, Switzerland. From July 1 to December 31, 2021, wastewater samples were collected six times a week from the inflow of the local wastewater treatment plant that receives wastewater from the catchment area of the city of Basel, Switzerland, comprising 273,075 inhabitants. The number of SARS-CoV-2 RNA copies was determined by reverse transcriptase-quantitative PCR. Spearman's rank correlation coefficients were calculated to determine correlations with the median seven-day incidence of genome copies per litre of wastewater and official case data. To explore delayed correlation effects between the seven-day median number of genome copies/litre wastewater and the median seven-day incidence of SARS-CoV-2 cases, time-lagged Spearman's rank correlation coefficients were calculated for up to 14 days. RNA extracts from daily wastewater samples were used to genotype circulating SARS-CoV-2 variants by next-generation sequencing. The number of daily cases and the median seven-day incidence of SARS-CoV-2 infections in the catchment area showed a high correlation with SARS-CoV-2 measurements in wastewater samples. All correlations between the seven-day median number of genome copies/litre wastewater and the time-lagged median seven-day incidence of SARS-CoV-2 cases were significant (p<0.001) for the investigated lag of up to 14 days. Correlation coefficients declined constantly from the maximum of 0.9395 on day 1 to the minimum of 0.8016 on day 14. The B.1.1.529 variant Omicron was detected in wastewater samples collected on November 21, 2021, before its official acknowledgement in a clinical sample by health authorities. In this proof-of-concept study, wastewater-based epidemiology proved a reliable and sensitive surveillance approach, complementing routine clinical testing for mapping COVID-19 pandemic dynamics and observing newly circulating SARS-CoV-2 variants.

COVIDHunter: COVID-19 Pandemic Wave Prediction and Mitigation via Seasonality Aware Modeling.

Early detection and isolation of COVID-19 patients are essential for successful implementation of mitigation strategies and eventually curbing the disease spread. With a limited number of daily COVID-19 tests performed in every country, simulating the COVID-19 spread along with the potential effect of each mitigation strategy currently remains one of the most effective ways in managing the healthcare system and guiding policy-makers. We introduce COVIDHunter, a flexible and accurate COVID-19 outbreak simulation model that evaluates the current mitigation measures that are applied to a region, predicts COVID-19 statistics (the daily number of cases, hospitalizations, and deaths), and provides suggestions on what strength the upcoming mitigation measure should be. The key idea of COVIDHunter is to quantify the spread of COVID-19 in a geographical region by simulating the average number of new infections caused by an infected person considering the effect of external factors, such as environmental conditions (e.g., climate, temperature, humidity), different variants of concern, vaccination rate, and mitigation measures. Using Switzerland as a case study, COVIDHunter estimates that we are experiencing a deadly new wave that will peak on 26 January 2022, which is very similar in numbers to the wave we had in February 2020. The policy-makers have only one choice that is to increase the strength of the currently applied mitigation measures for 30 days. Unlike existing models, the COVIDHunter model accurately monitors and predicts the daily number of cases, hospitalizations, and deaths due to COVID-19. Our model is flexible to configure and simple to modify for modeling different scenarios under different environmental conditions and mitigation measures. We release the source code of the COVIDHunter implementation at https://github.com/CMU-SAFARI/COVIDHunter and show how to flexibly configure our model for any scenario and easily extend it for different measures and conditions than we account for.

Forecasting the spread of the COVID-19 pandemic in Kenya using SEIR and ARIMA models.

COVID-19, a coronavirus disease 2019, is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first case in Kenya was identified on March 13, 2020, with the pandemic increasing to about 237,000 confirmed cases and 4,746 deaths by August 2021. We developed an SEIR model forecasting the COVID-19 pandemic in Kenya using an Autoregressive Integrated moving averages (ARIMA) model. The average time difference between the peaks of wave 1 to wave 4 was observed to be about 130 days. The 4th wave was observed to have had the least number of daily cases at the peak. According to the forecasts made for the next 60 days, the pandemic is expected to continue for a while. The 4th wave peaked on August 26, 2021 (498th day). By October 26, 2021 (60th day), the average number of daily infections will be 454 new cases and 40 severe cases, which would require hospitalization, and 16 critically ill cases requiring intensive care unit services. The findings of this study are key in developing informed mitigation strategies to ensure that the pandemic is contained and inform the preparedness of policymakers and health care workers.

Determining the Efficacy of Various Governmental Interventions in Stemming the Spread of SARS-CoV-2 in Seven Locations.

IntroductionSince the beginning of the COVID-19 pandemic, the efficacy of various governmental interventions in stemming the spread of SARS-CoV-2 has been the source of much public debate. Through identification of those specific governmental interventions which have been most effective in stemming the spread of SARS-CoV-2, this research has the potential to help guide future governmental responses to the COVID-19 pandemic.ObjectiveTo determine the efficacy of various governmental interventions in stemming the spread of SARS-CoV-2.MethodsData pertaining to the number of daily cases of COVID-19 between February or March and August 15th, 2020 were collected from New York, Texas, Florida, Louisiana, California, Italy, and Sweden. We plotted these datasets as epidemiological curves and superimposed various governmental interventions onto them. We also superimposed May 25th onto the domestic curves to reflect mass gatherings following the death of George Floyd and to determine their effect on the spread of SARS-CoV-2.ResultsIn New York, Louisiana, Texas, Florida, and Italy, various governmental interventions appear to have had meaningful impact on the number of daily cases recorded and prevention of future outbreaks. California seemingly did not gain much benefit from these interventions. Sweden, despite no governmental intervention, experienced only a transient surge in daily cases.ConclusionsGovernmental interventions were largely effective in stemming the spread of SARS-CoV-2. Stay-at-home orders proved efficacious in decreasing daily cases quickly during ongoing outbreaks. Social distancing measures and mask mandates, meanwhile, proved to be effective in preventing subsequent outbreaks, at lesser economic and psychological costs.

Sampling Inspection Plan to Test Daily COVID-19 Cases Using Gamma Distribution under Indeterminacy Based on Multiple Dependent Scheme.

The purpose of this paper is to develop a multiple dependent state (MDS) sampling plan based on time-truncated sampling schemes for the daily number of cases of the coronavirus disease COVID-19 using gamma distribution under indeterminacy. The proposed sampling scheme parameters include average sample number (ASN) and accept and reject sample numbers when the indeterminacy parameter is known. In addition to the parameters of the proposed sampling schemes, the resultant tables are provided for different known indeterminacy parametric values. The outcomes resulting from various sampling schemes show that the ASN decreases as indeterminacy values increase. This shows that the indeterminacy parameter plays a vital role for the ASN. A comparative study between the proposed sampling schemes and existing sampling schemes based on indeterminacy is also discussed. The projected sampling scheme is illustrated with the help of the daily number of cases of COVID-19 data. From the results and real example, we conclude that the proposed MDS sampling scheme under indeterminacy requires a smaller sample size compared to the single sampling plan (SSP) and the existing MDS sampling plan.

The functions of social indicators in the elaboration of public policy proposals. Case study: Public health policies

The identification and measurement of social indicators are essential elements in defining the public issue, which represent a first important step for the elaboration of public policies proposals. In this respect, regarding COVID-19 Pandemic public policies strategists, both of National Governments and international organizations measured each social indicator that could be integrated in the program of measures to control SARS-COV-2 virus. Therefore, indicators such as total number or daily number of cases, number of active / closed cases, number of new cases, number of people in ICU, rete of cases per 1 mil. Inhabitants, incident at community, local, national, continental and global level, number of performed tests and so on, were used by public central and local administrations in order to apply the measurements for fighting the pandemic. In this paperwork we aim to measure social perception on the efficiency of public health policies elaborated and deployed by Romanian Government, as well as the degree of transparency in communication of the public institutions in relationship with citizens.

‘Antigen Rapid Test’ Image-Processing based Machine Learning Algorithm for ART Buddy

2021 witnessed subsequent waves of COVID-19 sweeping across the world. As the number of daily cases rose in many countries, many adopted the utilization of antigen rapid test (ART) kits for faster detection and isolation of the infected. However, the accuracy of the ART can be impacted by incorrect usage and self-reporting biases. Despite self-administration, image processing of submitted images could be leveraged for validation. Given the ubiquitous use of the smartphone camera, mobile applications that included features such as user uploading of ART kit result images, facilitate verification by backend servers against incorrect self-reported ART results while improving compliance rates. For this purpose, we describe an algorithm that was incorporated into the ‘ART Buddy’ app for the classification of submitted positive and negative ART images. The algorithm was based on machine learning using the Convolutional Neural Network (CNN) to achieve an accuracy of 97.57%, precision of 79.31%, and recall of 88.46%.

Comparison of the COVID-19 Pandemic Waves in 2020 and 2021

The daily number of new COVID-19 cases and deaths per capita are important characteristics of the pandemic dynamics indicating the appearance of new coronavirus strains, the effectiveness of quarantine, testing and vaccination. The second year of the pandemic allowed us to compare the averaged daily number of cases and deaths in the period from September to December 2020 with the same period in 2021 in some northern countries and regions: Ukraine, EU, the UK, USA, some countries located in tropical zone and south hemisphere: India, Brazil, South Africa and Argentina and in the whole world in order to find some correlations with seasonal factors, the percentage of the fully vaccinated people and boosters. The results showed that existing vaccines cannot prevent new infections and vaccinated people can spread the infection as intensively as non-vaccinated ones, but vaccinations can diminish the numbers of death per capita

Modeling COVID‐19: Forecasting and analyzing the dynamics of the outbreaks in Hubei and Turkey

As the pandemic of Coronavirus Disease 2019 (COVID‐19) rages worldwide, accurate modeling of the dynamics thereof is essential. However, since the availability and quality of data vary dramatically from region to region, accurate modeling directly from a global perspective is difficult. Nevertheless, via local data collected by certain regions, it is possible to develop accurate local prediction tools, which may be coupled to develop global models. In this study, we analyze the dynamics of local outbreaks of COVID‐19 via a system of ordinary differential equations (ODEs). Utilizing a large amount of data available from the ebbing outbreak in Hubei, China, as a testbed, we predict the trajectory of daily cases, daily deaths, and other features of the Hubei outbreak. Through numerical experiments, we observe the effects of social distancing on the dynamics of the outbreak. Using knowledge gleaned from the Hubei outbreak, we apply our model to analyze the dynamics of the outbreak in Turkey. We provide forecasts for the peak of the outbreak and the daily number of cases and deaths in Turkey, by varying levels of social distancing and the transition rate which is from infected class to confirmed infected class.

Back to basics: measuring the impact of interventions to limit the spread of COVID-19 in Europe

BackgroundFollowing the emergence of the COVID-19 pandemic in Europe at the start of 2020, most countries had implemented various measures in an attempt to control the spread of the virus. This study analyses the main non-pharmaceutical interventions and their impact on the rate by which cumulative cases and deaths were growing in Europe during the first wave of this pandemic.MethodsThe interventions analysed are the school closures, restrictions on travel, cancellation of events, restrictions on gatherings, partial and full lockdowns. Data was collected on the implementation date of these interventions, and the number of daily cases and deaths during the first wave of the pandemic for every country and territory geographically located in Europe. The study uses growth rates to calculate the increase in cumulative cases and deaths in Europe before, during, and after these interventions were implemented.ResultsThe results show that decisions to close schools, cancel events, and restrict travel were taken during the same time period, whereas the decisions for the other interventions were taken when the growth rates were similar. The most effective interventions at lowering the rate by which cumulative cases were increasing were the travel restrictions, school closures, and the partial lockdown, while most effective against cumulative deaths were the partial lockdown, travel restrictions, and full lockdown.ConclusionAll the interventions reduced the rate by which cumulative cases and deaths were increasing with the partial lockdowns being the most effective from the other interventions, during the first wave of the pandemic in Europe.

Risk assessment of COVID-19 epidemic resurgence in relation to SARS-CoV-2 variants and vaccination passes

The introduction of COVID-19 vaccination passes (VPs) by many countries coincided with the Delta variant fast becoming dominant across Europe. A thorough assessment of their impact on epidemic dynamics is still lacking. Here, we propose the VAP-SIRS model that considers possibly lower restrictions for the VP holders than for the rest of the population, imperfect vaccination effectiveness against infection, rates of (re-)vaccination and waning immunity, fraction of never-vaccinated, and the increased transmissibility of the Delta variant. Some predicted epidemic scenarios for realistic parameter values yield new COVID-19 infection waves within two years, and high daily case numbers in the endemic state, even without introducing VPs and granting more freedom to their holders. Still, suitable adaptive policies can avoid unfavorable outcomes. While VP holders could initially be allowed more freedom, the lack of full vaccine effectiveness and increased transmissibility will require accelerated (re-)vaccination, wide-spread immunity surveillance, and/or minimal long-term common restrictions.

Estimation of R(t) based on illness onset data: An analysis of 1907–1908 smallpox epidemic in Tokyo

R(t), the actual average number of secondary cases per primary case at calendar time t, is epidemiologically useful in assessing transmission dynamics in a population with varying susceptibility levels. However, a technical limitation of existing methods of estimating R(t) is the reliance on the daily number of cases with illness onset and the distribution of the serial interval, although the estimator of R(t) should be calculated as the ratio of newly infected cases at time t to the total number of potentially infectious people at the same time. Using historical data of a smallpox outbreak in Tokyo City, Japan, approximately 100 years ago, we propose a new method to compute R(t) that can be estimated using information on illness onset. Our method decomposes the mechanism of transmission into two distinct pieces of information: the frequency of secondary transmission relative to disease age and the probability density function of the incubation period. Employing a piecewise constant model, our maximum likelihood estimates of R(t) dropped below unity (0.6; 95% confidence interval: 0.5–0.7) for the period from Day 64 to Day 79, indicating that the epidemic was under control in this period. R(t) was continuously below one through the remaining days. The model prediction captured the overall observed pattern of the epidemic well. Our method is appropriate for acute infectious diseases other than smallpox for which variations in infectivity relative to disease age should be considered to correctly estimate the transmission potential, such as the ongoing global epidemic of coronavirus disease 2019 (COVID-19).

A temporal study of Brazilian pregnant and postpartum women vulnerability for COVID-19: Characteristics, risk factors and outcomes

BackgroundDuring the COVID-19 second wave in Brazil, there has been a significant increase in the number of daily cases and deaths, including pregnant and postpartum women. We assess risk factors and outcomes for this priority group compared to the COVID-19 non-pregnant cohort in two epidemic waves.MethodsIn this retrospective cohort study we evaluated data of hospitalized pregnant, postpartum, and nonpregnant women aged 15-44 years, between epidemiological weeks 2020–8 and 2021–15, who tested positive for SARS-CoV-2, retrieved from the Influenza Epidemiological Surveillance Information System maintained by Ministry of Health of Brazil. We analysed in-hospital case fatality rate, crude and adjusted risk ratios on different outcomes aiming to compare data in two waves.FindingsThe study included pregnant women (n = 7,132), postpartum women (n = 2,405) and nonpregnant women (n = 76,278) hospitalized with COVID-19. Case fatality rates of pregnant women were lower in both waves compared to nonpregnant women, but higher among postpartum women. The risk for admission to the intensive care unit and invasive mechanical ventilation requirement in both waves was significantly higher among postpartum women compared to nonpregnant women. Cardiac disease, diabetes, obesity, and asthma were the most frequent underlying medical conditions in all patient groups. These comorbidities were significantly less frequent among pregnant women.InterpretationPregnant women with COVID-19 are at lower risk of poor outcome compared to nonpregnant women. On the other hand, postpartum women are at higher risk of adverse outcomes compared to pregnant and nonpregnant women, especially during the second wave. There was a significant increase in the in-hospital case fatality rate for all patient groups during the second wave of COVID-19.FundingThis study was financed in part by CAPES, CNPq, FAPEMIG and UFSJ.

Hybrid Modeling of Regional COVID-19 Transmission Dynamics in the U.S.

The fast transmission rate of COVID-19 worldwide has made this virus the most important challenge of year 2020. Many mitigation policies have been imposed by the governments at different regional levels (country, state, county, and city) to stop the spread of this virus. Quantifying the effect of such mitigation strategies on the transmission and recovery rates, and predicting the rate of new daily cases are two crucial tasks. In this paper, we propose a hybrid modeling framework which not only accounts for such policies but also utilizes the spatial and temporal information to characterize the pattern of COVID-19 progression. Specifically, a piecewise susceptible-infected-recovered (SIR) model is developed while the dates at which the transmission/recover rates change significantly are defined as "break points" in this model. A novel and data-driven algorithm is designed to locate the break points using ideas from fused lasso and thresholding. In order to enhance the forecasting power and to describe additional temporal dependence among the daily number of cases, this model is further coupled with spatial smoothing covariates and vector auto-regressive (VAR) model. The proposed model is applied to several U.S. states and counties, and the results confirm the effect of "stay-at-home orders" and some states' early "re-openings" by detecting break points close to such events. Further, the model provided satisfactory short-term forecasts of the number of new daily cases at regional levels by utilizing the estimated spatio-temporal covariance structures. They were also better or on par with other proposed models in the literature, including flexible deep learning ones. Finally, selected theoretical results and empirical performance of the proposed methodology on synthetic data are reported which justify the good performance of the proposed method.