Transmission Dynamics of COVID-19 in Ghana and the Impact of Public Health Interventions.
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ABSTRACT:
SUBMITTER: Ofori SK
PROVIDER: S-EPMC9294683 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesWe aimed to estimate the time-varying transmission dynamics of COVID-19 in China, Wuhan City, and Guangdong province, and compare to that of severe acute respiratory syndrome (SARS).MethodsData on COVID-19 cases in China up to 20 March 2020 was collected from epidemiological investigations or official websites. Data on SARS cases in Guangdong Province, Beijing, and Hong Kong during 2002-3 was also obtained. We estimated the doubling time, basic reproduction number (R0), and time-varying reproduction number (Rt) of COVID-19 and SARS.ResultsAs of 20 March 2020, 80,739 locally acquired COVID-19 cases were identified in mainland China, with most cases reported between 20 January and 29 February 2020. The R0 value of COVID-19 in China and Wuhan was 5.0 and 4.8, respectively, which was greater than the R0 value of SARS in Guangdong (R0 = 2.3), Hong Kong (R0 = 2.3), and Beijing (R0 = 2.6). At the start of the COVID-19 epidemic, the Rt value in China peaked at 8.4 and then declined quickly to below 1.0 in one month. With SARS, the Rt curve saw fluctuations with more than one peak, the highest peak was lower than that for COVID-19.ConclusionsCOVID-19 has much higher transmissibility than SARS, however, a series of prevention and control interventions to suppress the outbreak were effective. Sustained efforts are needed to prevent the rebound of the epidemic in the context of the global pandemic.
| S-EPMC7836966 | biostudies-literature
Project description:BACKGROUND:It is unclear whether seasonal changes, school closures or other public health interventions will result in a slowdown of the current coronavirus disease 2019 (COVID-19) pandemic. We aimed to determine whether epidemic growth is globally associated with climate or public health interventions intended to reduce transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS:We performed a prospective cohort study of all 144 geopolitical areas worldwide (375 609 cases) with at least 10 COVID-19 cases and local transmission by Mar. 20, 2020, excluding China, South Korea, Iran and Italy. Using weighted random-effects regression, we determined the association between epidemic growth (expressed as ratios of rate ratios [RRR] comparing cumulative counts of COVID-19 cases on Mar. 27, 2020, with cumulative counts on Mar. 20, 2020) and latitude, temperature, humidity, school closures, restrictions of mass gatherings, and measures of social distancing during an exposure period 14 days previously (Mar. 7 to 13, 2020). RESULTS:In univariate analyses, there were few or no associations of epidemic growth with latitude and temperature, but weak negative associations with relative humidity (RRR per 10% 0.91, 95% confidence interval [CI] 0.85-0.96) and absolute humidity (RRR per 5 g/m3 0.92, 95% CI 0.85-0.99). Strong associations were found for restrictions of mass gatherings (RRR 0.65, 95% CI 0.53-0.79), school closures (RRR 0.63, 95% CI 0.52-0.78) and measures of social distancing (RRR 0.62, 95% CI 0.45-0.85). In a multivariable model, there was a strong association with the number of implemented public health interventions (p for trend = 0.001), whereas the association with absolute humidity was no longer significant. INTERPRETATION:Epidemic growth of COVID-19 was not associated with latitude and temperature, but may be associated weakly with relative or absolute humidity. Conversely, public health interventions were strongly associated with reduced epidemic growth.
| S-EPMC7259972 | biostudies-literature
Project description:BackgroundThe widespread pandemic of novel coronavirus disease 2019 (COVID-19) poses an unprecedented global health crisis. In the United States (US), different state governments have adopted various combinations of non-pharmaceutical public health interventions (NPIs), such as non-essential business closures and gathering bans, to mitigate the epidemic from February to April, 2020. Quantitative assessment on the effectiveness of NPIs is greatly needed to assist in guiding individualized decision making for adjustment of interventions in the US and around the world. However, the impacts of these approaches remain uncertain.MethodsBased on the reported cases, the effective reproduction number (Rt) of COVID-19 epidemic for 50 states in the US was estimated. Measurements on the effectiveness of nine different NPIs were conducted by assessing risk ratios (RRs) between Rt and NPIs through a generalized linear model (GLM).ResultsDifferent NPIs were found to have led to different levels of reduction in Rt. Stay-at-home contributed approximately 51% (95% CI 46-57%), wearing (face) masks 29% (15-42%), gathering ban (more than 10 people) 19% (14-24%), non-essential business closure 16% (10-21%), declaration of emergency 13% (8-17%), interstate travel restriction 11% (5-16%), school closure 10% (7-14%), initial business closure 10% (6-14%), and gathering ban (more than 50 people) 7% (2-11%).ConclusionsThis retrospective assessment of NPIs on Rt has shown that NPIs played critical roles on epidemic control in the US in the past several months. The quantitative results could guide individualized decision making for future adjustment of NPIs in the US and other countries for COVID-19 and other similar infectious diseases.
| S-EPMC8139542 | biostudies-literature
Project description:The COVID-19 pandemic has sparked unprecedented public health and social measures (PHSM) by national and local governments, including border restrictions, school closures, mandatory facemask use and stay at home orders. Quantifying the effectiveness of these interventions in reducing disease transmission is key to rational policy making in response to the current and future pandemics. In order to estimate the effectiveness of these interventions, detailed descriptions of their timelines, scale and scope are needed. The Health Intervention Tracking for COVID-19 (HIT-COVID) is a curated and standardized global database that catalogues the implementation and relaxation of COVID-19 related PHSM. With a team of over 200 volunteer contributors, we assembled policy timelines for a range of key PHSM aimed at reducing COVID-19 risk for the national and first administrative levels (e.g. provinces and states) globally, including details such as the degree of implementation and targeted populations. We continue to maintain and adapt this database to the changing COVID-19 landscape so it can serve as a resource for researchers and policymakers alike.
| S-EPMC7453020 | biostudies-literature
Project description: Not available
| S-EPMC8385050 | biostudies-literature
Project description:BackgroundIn the Republic of Korea (ROK), social distancing and public behavior changes mitigated COVID-19 spread. However, a second wave of the epidemic is expected in the fall if neither vaccine nor antiviral drugs become available. This study investigated the impact of non-pharmaceutical measures on short- and long-term outbreak dynamics.MethodsA mathematical model based on Susceptible-Exposed-Infectious-Recovered model is developed considering isolated and behavior-changed groups. Using the least-squares fitting method, transmission and behavior change rates were estimated using cases reported from February 16 to April 20, 2020.FindingsThe estimated transmission rate of COVID-19 was 4·6180 and behavior change rate was 2·6044. The model predicted the number of new cases to continuously decrease, with less than one case expected after May 6, 2020. Concurrently, a 25% reduction in behavioral changes during the outbreak would increase the case count by 60,000, resulting in 4,000 cases at maximum, exceeding the medical system's capacity. As behavioral restrictions are eased, local transmission will likely increase, with forecasted second wave peak in October 2020.InterpretationSocial distancing and public behavior changes have curbed the spread of COVID-19 in the ROK. Mathematical modeling demonstrates the importance of these measures in reducing and delaying outbreaks. Nevertheless, non-pharmaceutical interventions cannot eliminate the disease. In the future, vaccines and antiviral treatments combined with social distancing and public behavior changes will be paramount to ending COVID-19 epidemic.
| S-EPMC7514094 | biostudies-literature
Project description:Since the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has been quickly spreading out to other provinces and neighboring countries. Estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity. A deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and intervention measures. The estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6.47 (95% CI 5.71–7.23). Sensitivity analyses show that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction adopted by Wuhan on 2019-nCoV infection in Beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value. It is essential to assess how the expensive, resource-intensive measures implemented by the Chinese authorities can contribute to the prevention and control of the 2019-nCoV infection, and how long they should be maintained. Under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 January 2020) with a significant low peak value. With travel restriction (no imported exposed individuals to Beijing), the number of infected individuals in seven days will decrease by 91.14% in Beijing, compared with the scenario of no travel restriction.
2024-09-02 | BIOMD0000000971 | BioModels
Project description:IntroductionIn response to the COVID-19 pandemic, health systems quickly implemented changes in care delivery with a goal of balancing patient-focused obstetric care with the need to protect pregnant persons and health care providers from infection. Yet, there is no consensus within the scientific community on the impact these measures have on obstetric outcomes in vulnerable populations. We aimed to assess the impact of the COVID-19 pandemic on rates of obstetric procedures and severe maternal morbidity (SMM) among births at an urban safety net institution.MethodsWe used an interrupted time series design to calculate risk ratios (RRs) and 95% confidence intervals (CIs) comparing monthly rates of labor induction, cesarean births (overall and among nulliparous, term, singleton, vertex births), operative vaginal births, and SMM among births occurring at a public hospital before (March 1, 2016, to February 29, 2020) and during (March 1, 2020, to May 31, 2021) the COVID-19 pandemic.ResultsThere were 10,714 and 2,736 births in the prepandemic and postpandemic periods, respectively. Overall, the rates of obstetric interventions and SMM were constant over the two time periods. There were no significant differences in rates of labor induction (42% during prepandemic period vs. 45% during pandemic period; RR, 1.12; 95% CI, 0.93-1.34), operative vaginal births (5% vs. 6%; RR, 1.24; 95% CI, 0.88-1.76), cesarean births (28% vs. 33%; RR, 1.10; 95% CI, 0.94-1.28), or nulliparous, term, singleton, vertex cesarean births (24% vs. 31%; RR, 1.27; 95% CI, 0.92-1.74). Rates of SMM (7% vs. 8%; RR, 1.19; 95% CI, 0.86-1.65) were also unchanged.ConclusionsOur findings indicate that the rapid implementation of measures to reduce viral transmission in the labor and delivery setting did not materially affect routine clinical management or rates of serious maternal complications.
| S-EPMC9381428 | biostudies-literature
Project description:BackgroundEstimation of temporal changes in human immunodeficiency virus (HIV) transmission patterns can help to elucidate the impact of preventive strategies and public health policies.MethodsPortuguese HIV-1 subtype B and G pol genetic sequences were appended to global reference data sets to identify country-specific transmission clades. Bayesian birth-death models were used to estimate subtype-specific effective reproductive numbers (Re). Discrete trait analysis (DTA) was used to quantify mixing among transmission groups.ResultsWe identified 5 subtype B Portuguese clades (26-79 sequences) and a large monophyletic subtype G Portuguese clade (236 sequences). We estimated that major shifts in HIV-1 transmission occurred around 1999 (95% Bayesian credible interval [BCI], 1998-2000) and 2000 (95% BCI, 1998-2001) for subtypes B and G, respectively. For subtype B, Re dropped from 1.91 (95% BCI, 1.73-2.09) to 0.62 (95% BCI,.52-.72). For subtype G, Re decreased from 1.49 (95% BCI, 1.39-1.59) to 0.72 (95% BCI, .63-.8). The DTA suggests that people who inject drugs (PWID) and heterosexuals were the source of most (>80%) virus lineage transitions for subtypes G and B, respectively.ConclusionsThe estimated declines in Re coincide with the introduction of highly active antiretroviral therapy and the scale-up of harm reduction for PWID. Inferred transmission events across transmission groups emphasize the importance of prevention efforts for bridging populations.
| S-EPMC6581889 | biostudies-literature
Project description:(1) Background: During the global spread of COVID-19, Japan has been among the top countries to maintain a relatively low number of infections, despite implementing limited institutional interventions and its high population density. This study investigated how limited intervention policies have affected public health and economic conditions in the COVID-19 context and aimed to gain insight into the effective and sustainable measures against new infectious diseases in densely inhabited areas. (2) Methods: A system dynamics approach was employed. Qualitative causal loop analysis and stock and quantitative flow model analysis were performed, using a Tokyo Metropolitan area dataset. (3) Results: A causal loop analysis suggested that there were risks in prematurely terminating such interventions. Based on this result and the subsequent quantitative modeling, we found that the short-term effectiveness of a short-term pre-emptive stay-at-home request caused a resurgence in the number of positive cases, whereas an additional request provided a limited negative add-on effect for economic measures (e.g., number of electronic word-of-mouth communications and restaurant visits). (4) Conclusions: These findings suggest the superiority of a mild and continuous intervention as a long-term countermeasure under epidemic pressures when compared with strong intermittent interventions.
| S-EPMC8398311 | biostudies-literature