Impact of Governmental interventions on epidemic progression and workplace activity during the COVID-19 outbreak
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ABSTRACT:
SUBMITTER: Ram S
PROVIDER: S-EPMC8578600 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In January 2020, a COVID-19 outbreak was detected in Sichuan Province of China. Six weeks later, the outbreak was successfully contained. The aim of this work is to characterize the epidemiology of the Sichuan outbreak and estimate the impact of interventions in limiting SARS-CoV-2 transmission. We analyzed patient records for all laboratory-confirmed cases reported in the province for the period of January 21 to March 16, 2020. To estimate the basic and daily reproduction numbers, we used a Bayesian framework. In addition, we estimated the number of cases averted by the implemented control strategies. The outbreak resulted in 539 confirmed cases, lasted less than two months, and no further local transmission was detected after February 27. The median age of local cases was 8 years older than that of imported cases. We estimated R0 at 2.4 (95% CI: 1.6-3.7). The epidemic was self-sustained for about 3 weeks before going below the epidemic threshold 3 days after the declaration of a public health emergency by Sichuan authorities. Our findings indicate that, were the control measures be adopted four weeks later, the epidemic could have lasted 49 days longer (95% CI: 31-68 days), causing 9,216 more cases (95% CI: 1,317-25,545).
| S-EPMC7794025 | biostudies-literature
Project description:BackgroundThe coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.ObjectiveIn this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.MethodsWe developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days.ResultsML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection.ConclusionThe study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic.
| S-EPMC10764024 | biostudies-literature
Project description:BackgroundTo reduce the transmission of the severe acute respiratory syndrome coronavirus 2 in its first wave, European governments have implemented successive measures to encourage social distancing. However, it remained unclear how effectively measures reduced the spread of the virus. We examined how the effective-contact rate (ECR), the mean number of daily contacts for an infectious individual to transmit the virus, among European citizens evolved during this wave over the period with implemented measures, disregarding a priori information on governmental measures.MethodsWe developed a data-oriented approach that is based on an extended Susceptible-Exposed-Infectious-Removed (SEIR) model. Using the available data on the confirmed numbers of infections and hospitalizations, we first estimated the daily total number of infectious-, exposed- and susceptible individuals and subsequently estimated the ECR with an iterative Poisson regression model. We then compared change points in the daily ECRs to the moments of the governmental measures.ResultsThe change points in the daily ECRs were found to align with the implementation of governmental interventions. At the end of the considered time-window, we found similar ECRs for Italy (0.29), Spain (0.24), and Germany (0.27), while the ECR in the Netherlands (0.34), Belgium (0.35) and the UK (0.37) were somewhat higher. The highest ECR was found for Sweden (0.45).ConclusionsThere seemed to be an immediate effect of banning events and closing schools, typically among the first measures taken by the governments. The effect of additionally closing bars and restaurants seemed limited. For most countries a somewhat delayed effect of the full lockdown was observed, and the ECR after a full lockdown was not necessarily lower than an ECR after (only) a gathering ban.
| S-EPMC7908011 | biostudies-literature
Project description:Non-pharmaceutical interventions to combat COVID-19 transmission have worked to slow the spread of the epidemic but can have high socio-economic costs. It is critical we understand the efficacy of non-pharmaceutical interventions to choose a safe exit strategy. Many current models are not suitable for assessing exit strategies because they do not account for epidemic resurgence when social distancing ends prematurely (e.g., statistical curve fits) nor permit scenario exploration in specific locations. We developed an SEIR-type mechanistic epidemiological model of COVID-19 dynamics to explore temporally variable non-pharmaceutical interventions. We provide an interactive tool and code to estimate the transmission parameter, β, and the effective reproduction number, R eff . We fit the model to Santa Clara County, California, where an early epidemic start date and early shelter-in-place orders could provide a model for other regions. As of April 22, 2020, we estimate an R eff of 0.982 (95% CI: 0.849 - 1.107) in Santa Clara County. After June 1 (the end-date for Santa Clara County shelter-in-place as of April 27), we estimate a shift to partial social distancing, combined with rigorous testing and isolation of symptomatic individuals, is a viable alternative to indefinitely maintaining shelter-in-place. We also estimate that if Santa Clara County had waited one week longer before issuing shelter-in-place orders, 95 additional people would have died by April 22 (95% CI: 7 - 283). Given early life-saving shelter-in-place orders in Santa Clara County, longer-term moderate social distancing and testing and isolation of symptomatic individuals have the potential to contain the size and toll of the COVID-19 pandemic in Santa Clara County, and may be effective in other locations.
| S-EPMC7276010 | biostudies-literature
Project description:Rapid responses in the early stage of a new epidemic are crucial in outbreak control. Public holidays for outbreak control could provide a critical time window for a rapid rollout of social distancing and other control measures at a large population scale. The objective of our study was to explore the impact of the timing and duration of outbreak-control holidays on the coronavirus disease 2019 (COVID-19) epidemic spread during the early stage in China. We developed a compartment model to simulate the dynamic transmission of COVID-19 in China starting from January 2020. We projected and compared epidemic trajectories with and without an outbreak-control holiday that started during the Chinese Lunar New Year. We considered multiple scenarios of the outbreak-control holiday with different durations and starting times, and under different assumptions about viral transmission rates. We estimated the delays in days to reach certain thresholds of infections under different scenarios. Our results show that the outbreak-control holiday in China likely stalled the spread of COVID-19 for several days. The base case outbreak-control holiday (21 d for Hubei Province and 10 d for all other provinces) delayed the time to reach 100 000 confirmed infections by 7.54 d. A longer outbreak-control holiday would have had stronger effects. A nationwide outbreak-control holiday of 21 d would have delayed the time to 100 000 confirmed infections by nearly 10 d. Furthermore, we find that outbreak-control holidays that start earlier in the course of a new epidemic are more effective in stalling epidemic spread than later holidays and that additional control measures during the holidays can boost the holiday effect. In conclusion, an outbreak-control holiday can likely effectively delay the transmission of epidemics that spread through social contacts. The temporary delay in the epidemic trajectory buys time, which scientists can use to discover transmission routes and identify effective public health interventions and which governments can use to build physical infrastructure, organize medical supplies, and deploy human resources for long-term epidemic mitigation and control efforts.
| S-EPMC7502241 | biostudies-literature
Project description:Summary This paper estimates the effect of nonpharmaceutical intervention policies on public health during the COVID-19 outbreak by considering a counterfactual case for Sweden. Using a synthetic control approach, I find that strict initial lockdown measures play an important role in limiting the spread of the COVID-19 infection, as the infection cases in Sweden would have been reduced by almost 75 percent had its policymakers followed stricter containment policies. As people dynamically adjust their behaviour in response to information and policies, the impact of nonpharmaceutical interventions becomes visible, with a time lag of around 5 weeks. Supplementary robustness checks and an alternative difference-in-differences framework analysis do not fundamentally alter the main conclusions. Finally, extending the analysis to excess mortality, I find that the lockdown measures would have been associated with a lower excess mortality rate in Sweden by 25 percentage points, with a steep age gradient of 29 percentage points for the most vulnerable elderly cohort. The outcome of this study can assist policymakers in laying out future guidelines to further protect public health, as well as facilitate plans for economic recovery.
| S-EPMC7499505 | biostudies-literature
Project description:Coronavirus disease 2019 (COVID-19) rapidly spread worldwide in the first quarter of 2020 and resulted in a global crisis. Investigation of the potential association of the spread of the COVID-19 infection with climate or ambient air pollution could lead to the development of preventive strategies for disease control. To examine this association, we conducted a longitudinal cohort study of 28 geographical areas of Japan with documented outbreaks of COVID-19. We analyzed data obtained from March 13 to April 6, 2020, before the Japanese government declared a state of emergency. The results revealed that the epidemic growth of COVID-19 was significantly associated with increase in daily temperature or sunshine hours. This suggests that an increase in person-to-person contact due to increased outing activities on a warm and/or sunny day might promote the transmission of COVID-19. Our results also suggested that short-term exposure to suspended particles might influence respiratory infections caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Further research by well-designed or well-controlled study models is required to ascertain this effect. Our findings suggest that weather has an indirect role in the transmission of COVID-19 and that daily adequate preventive behavior decreases the transmission.
| S-EPMC7420955 | biostudies-literature
Project description:ObjectiveIn this study, we investigated the impact of COVID-19 NPIs in South Africa to understand their effectiveness in the reduction of transmission of COVID-19 in the South African population. This study also investigated the COVID-19 testing, reporting, hospitalised cases, excess deaths and COVID-19 modelling in the first wave of the COVID-19 epidemic in South Africa.MethodsA semi-reactive stochastic COVID-19 model, the ARI COVID-19 SEIR model, was used to investigate the impact of NPIs in South Africa to understand their effectiveness in the reduction of COVID-19 transmission in the South African population. COVID-19 testing, reporting, hospitalised cases and excess deaths in the first COVID-19 epidemic wave in South Africa were investigated using regressional analysis and descriptive statistics.FindingsThe general trend in population movement in South African locations shows that the COVID-19 NPIs (National Lockdown Alert Levels 5,4,3,2) were approximately 30% more effective in reducing population movement concerning each increase by 1 Alert Level. The translated reduction in the effective SARS-CoV-2 daily contact number (β) was 6.12% to 36.1% concerning increasing Alert Levels. Due to the implemented NPIs, the effective SARS-CoV-2 daily contact number in the first COVID-19 epidemic wave in South Africa was reduced by 58.1-71.1% while the peak was delayed by 84 days. The estimated COVID-19 reproductive number was between 1.98 to 0.40. During South Africa's first COVID-19 epidemic wave, the mean COVID-19 admission status in South African hospitals was 58.5%, 95% CI [58.1-59.0] in the general ward, 13.4%, 95% CI [13.1-13.7] in the intensive care unit, 13.3%, 95% CI [12.6-14.0] on oxygen, 6.37%, 95% CI [6.23-6.51] in high care, 6.29%, 95% CI [6.02-6.55] on ventilator and 2.13%, 95% CI [1.87-2.43] in isolation ward respectively. The estimated mean South African COVID-19 patient discharge rate was 11.9 days per patient. While the estimated mean of the South African COVID-19 patient case fatality rate (CFR) in hospital and outside the hospital was 2.06%, 95% CI [1.86-2.25] (deaths per admitted patients) and 2.30%, 95% CI [1.12-3.83](deaths per severe and critical cases) respectively. The relatively high coefficient of variance in COVID-19 model outputs observed in this study shows the uncertainty in the accuracy of the reviewed COVID-19 models in predicting the severity of COVID-19. However, the reviewed COVID-19 models were accurate in predicting the progression of the first COVID-19 epidemic wave in South Africa.ConclusionThe results from this study show that the COVID-19 NPI policies implemented by the Government of South Africa played a significant role in the reduction of COVID-19 active, hospitalised cases and deaths in South Africa's first COVID-19 epidemic wave. The results also show the use of COVID-19 modelling to understand the COVID-19 pandemic and the impact of regressor variables in an epidemic.
| S-EPMC10403893 | biostudies-literature
Project description:Coronavirus disease 2019 (COVID-19) broke out first in Wuhan City, Hubei Province, China. In the process of controlling the pandemic, many Chinese medical staff (MS) were infected. We used government data, post mortem reports, and the medical literature on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, as well as prevention-and-control guidelines from the government, hospitals and media, to discuss the main risks factors faced by MS. We suggest that, when dealing with a similar pandemic in the future, guidance on personal protective equipment must be provided and materials reserved in advance. Also, the emergency response of medical institutions should be enhanced, and information shared with other countries facing identical severe challenges.
| S-EPMC7430766 | biostudies-literature