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Ali Hadianfar, Razieh Yousefi, Milad Delavary, , Mohammad Taghi Shakeri,
Published: 19 August 2021
Abstract:
Background Public health policies with varying degrees of restriction have been imposed around the world to prevent the spread of coronavirus disease 2019 (COVID-19). In this study, we aimed to evaluate the effects of the implementation of government policies and the Nowruz holidays on the containment of the COVID-19 pandemic in Iran, using an intervention time series analysis. Methods Daily data on COVID-19 cases registered between February 19 and May 2, 2020 were collected from the World Health Organization (WHO)’s website. Using an intervention time series modeling, the effect of two government policies on the number of confirmed cases were evaluated, namely the closing of schools and universities, and the implementation of social distancing measures. Furthermore, the effect of the Nowruz holidays as a non-intervention factor for the spread of COVID-19 was also analyzed. Results The results showed that, after the implementation of the first intervention, i.e., the closing of universities and schools, no statistically significant change was found in the number of new confirmed cases. The Nowruz holidays was followed by a significant increase in new cases (1,872.20; 95% CI, 1,257.60 to 2,476.79; p<0.001)), while the implementation of social distancing measures was followed by a significant decrease in such cases (2,182.80; 95% CI, 1,556.56 to 2,809.04; p<0.001). Conclusion The Nowruz holidays and the implementation of social distancing measures in Iran were related to a significant increase and decrease in COVID-19 cases, respectively. These results highlight the necessity of measuring the effect of health and social interventions for their future implementations.
, Natthaprang Nittayasoot, Panithee Thammawijaya, , Kumnuan Ungchusak
Risk Management and Healthcare Policy, pp 3197-3207; https://doi.org/10.2147/rmhp.s318012

Abstract:
Background: Thailand experienced the first wave of Coronavirus Disease 2019 (COVID-19) during March–May 2020 and has been facing the second wave since December 2020. The area facing the greatest impact was Samut Sakhon, a main migrant-receiving province in the country. The Department of Disease Control (DDC) of the Thai Ministry of Public Health (MOPH) considered initiating a vaccination strategy in combination with active case finding (ACF) in the epidemic area. The DDC commissioned a research team to predict the impact of various vaccination and ACF policy scenarios in terms of case reduction and deaths averted, which is the objective of this study. Methods: The design of this study was a secondary analysis of quantitative data. Most of the data were obtained from the DDC, MOPH. Deterministic system dynamics and compartmental models were exercised. A basic reproductive number (R0) was estimated at 3 from the beginning. Vaccine efficacy against disease transmission was assumed to be 50%. A total of 10,000 people were estimated as an initial population size. Results: The findings showed that the greater the vaccination coverage, the smaller the size of incident and cumulative cases. Compared with a no-vaccination and no-ACF scenario, the 90%-vaccination coverage combined with 90%-ACF coverage contributed to a reduction of cumulative cases by 33%. The case reduction benefit would be greater when R0 was smaller (∼ 53% and ∼ 51% when R0 equated 2 and 1.5, respectively). Conclusion: This study reaffirmed the idea that a combination of vaccination and ACF measures contributed to favourable results in reducing the number of COVID-19 cases and deaths, relative to the implementation of only a single measure. The greater the vaccination and ACF coverage, the greater the volume of cases saved. Though we demonstrated the benefit of vaccination strategies in this setting, actual implementation should consider many more policy angles, such as social acceptability, cost-effectiveness and operational feasibility. Further studies that address these topics based on empirical evidence are of great value.
Enrico Michelini, Nico Bortoletto,
Frontiers in Public Health, Volume 9; https://doi.org/10.3389/fpubh.2021.615745

Abstract:
Introduction: Mandated restrictions on outdoor physical activity (PA) during the coronavirus pandemic disrupted the lifeworld of millions of people and led to a contradictory situation. On the one hand, PA was perceived as risky behaviour, as it might facilitate transmission of the virus. On the other hand, while taking precautions, regular PA was an important tool to promote the population's health during the lockdown. Methods: This paper examines the differences in government restrictions on PA in France, Germany, and Italy during the first wave of the COVID-19 pandemic. We draw on techniques of qualitative content analysis and apply a critical theoretical framework to assess the countries' restrictions on PA. Results: Our analysis shows that the restrictions on PA varied in the three countries, in all three countries. This variance is attributed both to differences in the timing and severity of the pandemic in the countries analysed, as well as to the divergence in the relationships between the countries' sport and health systems. Conclusion: At the national level, the variance in restrictions on PA reflect the differences in the spread of the coronavirus and in the health systems' understanding of and approach to PA. The global scientific discourse on the pandemic represents a further key influencing factor. The management of the coronavirus pandemic has demonstrated that the extreme complexity of societies in terms of public health, politics, and the economy pose challenges and unsolvable contradictions.
, Marlen Chawani, Melody Sakala, Lily Mwandira, Elias Phiri, Edith Milanzi, Mphatso Dennis Phiri, Isabel Kazanga, Thomasena O’Byrne, Eliya M Zulu, et al.
Published: 18 May 2021
by BMJ
BMJ Global Health, Volume 6; https://doi.org/10.1136/bmjgh-2021-006035

Abstract:
Malawi declared a state of national disaster due to the COVID-19 pandemic on 20th March 2020 and registered its first confirmed coronavirus case on the 2 April 2020. The aim of this paper was to document policy decisions made in response to the COVID-19 pandemic from January to August 2020. We reviewed policy documents from the Public Health Institute of Malawi, the Malawi Gazette, the Malawi Ministry of Health and Population and the University of Oxford Coronavirus Government Response Tracker. We found that the Malawi response to the COVID-19 pandemic was multisectoral and implemented through 15 focused working groups termed clusters. Each cluster was charged with providing policy direction in their own area of focus. All clusters then fed into one central committee for major decisions and reporting to head of state. Key policies identified during the review include international travel ban, school closures at all levels, cancellation of public events, decongesting workplaces and public transport, and mandatory face coverings and a testing policy covering symptomatic people. Supportive interventions included risk communication and community engagement in multiple languages and over a variety of mediums, efforts to improve access to water, sanitation, nutrition and unconditional social-cash transfers for poor urban and rural households.
, Shiva Yousefian, Amirhosein Bahramzadeh, Mohammad Hossein Vaziri
Published: 13 April 2021
BMC Public Health, Volume 21, pp 1-9; https://doi.org/10.1186/s12889-021-10806-9

Abstract:
Background In December 2019, the Chinese city of Wuhan reported a novel pneumonia caused by COVID-19. While the COVID-19 pandemic has been increasingly affecting the world, the occurrence of disasters resulted in complex emergencies. The present review is aimed to identify the literature focused on health system response to coincidence of COVID-19 and disasters as well as describing their finding, implications and lessons-learned. Methods This study was conducted and reported based on PRISMA guideline. The databases of Web of Sciences, PubMed, Scopus, Google Scholar and World Health Organization Library were searched. The inclusion criteria were all forms of published articles which investigated the coincidence of disasters and COVID-19 pandemic. Using the title and abstract screening, the selections of studies were performed by two researchers. Once, the relevant papers were finalized, the analysis was done in two parts of descriptive analysis and implications for health systems. Results Out of 1245 studies generated by initial search, a number of 13 articles was selected for final analysis. Earthquake was the most frequent disaster which its coincidence with COVID-19 was studied by researchers (31%). The implications of researchers for healthcare system were explained in three sections of climatic events, earthquakes and all hazard approach in relation to COVID-19. Conclusion Extracting the lessons learned from the regions affected by disasters at the time of COVID-19 pandemic can be helpful for healthcare professionals and policy-makers to improve their preparedness and response during disasters and a serious pandemic such as COVID-19. Further research is needed to identify the factors which strengthen the preparedness of health system for the dual risk of natural hazards and pandemics.
, Mariya Rahman, Nusrat Fahmida Trisha, Samia Tasnim, Tasmiah Nuzhath, Nishat Tasnim Hasan, Heather Clark, Arindam Das, E. Lisako J. McKyer, Helal Uddin Ahmed, et al.
Published: 5 April 2021
Abstract:
Introduction The COVID-19 pandemic has impacted biopsychosocial health and wellbeing globally. Pre-pandemic studies suggest a high prevalence of common mental disorders, including anxiety and depression in South Asian countries, which may aggravate during this pandemic. This systematic meta-analytic review was conducted to estimate the pooled prevalence of anxiety and depression in South Asian countries during the COVID-19 pandemic. Method We systematically searched for cross-sectional studies on eight major bibliographic databases and additional sources up to October 12, 2020, that reported the prevalence of anxiety or depression in any of the eight South Asian countries. A random-effects model was used to calculate the pooled proportion of anxiety and depression. Results A total of 35 studies representing 41,402 participants were included in this review. The pooled prevalence of anxiety in 31 studies with a pooled sample of 28,877 was 41.3% (95% confidence interval [CI]: 34.7–48.1, I2 = 99.18%). Moreover, the pooled prevalence of depression was 34.1% (95% CI: 28.9–39.4, I2 = 99%) among 37,437 participants in 28 studies. Among the South Asian countries, India had a higher number of studies, whereas Bangladesh and Pakistan had a higher pooled prevalence of anxiety and depression. No studies were identified from Afghanistan, Bhutan, and Maldives. Studies in this review had high heterogeneity, high publication bias confirmed by Egger's test, and varying prevalence rates across sub-groups. Conclusion South Asian countries have high prevalence rates of anxiety and depression, suggesting a heavy psychosocial burden during this pandemic. Clinical and public mental health interventions should be prioritized alongside improving the social determinants of mental health in these countries. Lastly, a low number of studies with high heterogeneity requires further research exploring the psychosocial epidemiology during COVID-19, which may inform better mental health policymaking and practice in South Asia.
Yiyin Chen, Sabra L. Klein, , Huifen Li, Cunjin Wu, Nicole M. Osevala, Taisheng Li, Joseph B. Margolick, Graham Pawelec,
Published: 31 October 2020
Ageing Research Reviews, Volume 65, pp 101205-101205; https://doi.org/10.1016/j.arr.2020.101205

Abstract:
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic was first reported in Wuhan, China in December 2019, moved across the globe at an unprecedented speed, and is having a profound and yet still unfolding health and socioeconomic impacts. SARS-CoV-2, a β-coronavirus, is a highly contagious respiratory pathogen that causes a disease that has been termed the 2019 coronavirus disease (COVID-19). Clinical experience thus far indicates that COVID-19 is highly heterogeneous, ranging from being asymptomatic and mild to severe and causing death. Host factors including age, sex, and comorbid conditions are key determinants of disease severity and progression. Aging itself is a prominent risk factor for severe disease and death from COVID-19. We hypothesize that age-related decline and dysregulation of immune function, i.e., immunosenescence and inflammaging play a major role in contributing to heightened vulnerability to severe COVID-19 outcomes in older adults. Much remains to be learned about the immune responses to SARS-CoV-2 infection. We need to begin partitioning all immunological outcome data by age to better understand disease heterogeneity and aging. Such knowledge is critical not only for understanding of COVID-19 pathogenesis but also for COVID-19 vaccine development.
, Rita W Y Ng, , Christopher K C Lai, Junjie Huang, , Siaw S Boon,
Published: 7 October 2020
by BMJ
BMJ Global Health, Volume 5; https://doi.org/10.1136/bmjgh-2020-003573

Abstract:
Introduction: An international city, Hong Kong, in proximity to the first epicentre of COVID- 19, experienced two epidemic waves with different importation pressure. We compared the epidemiological features of patients with COVID-19 in the context of containment policies between the first and second waves.Methods: We retrieved information on the first 1038 cases detected in Hong Kong (23 January to 25 April 2020) to analyse the epidemiological characteristics including age/gender-specific incidence, clustering, reproduction number (Rt) and containment delay; in relation to the containment measures implemented. Factors associated with containment delay were evaluated by multiple linear regression analysis with age, gender, epidemic wave and infection source as covariates. A time series of 5-day moving average was plotted to examine the changes across the two epidemic waves.Results: The incidence and mortality (135.5 and 0.5 per 1 000 000 population) was among the lowest in the world. Aggressive escalation of border control correlated with reductions in Rt from 1.35 to 0.57 and 0.92 to 0.18, and aversions of 450 and 1650 local infections during the first and second waves, respectively. Implementing COVID-19 tests for overseas returners correlated with an upsurge of asymptomatic case detection, and shortened containment delay in the second wave. Medium-sized cluster events in the first wave were family gatherings, whereas those in the second wave were leisure activities among youngsters. Containment delay was associated with older age (adjusted OR (AOR)=1.01, 95% CI 1.00 to 1.02, p=0.040), male gender (AOR=1.41, 95% CI 1.02 to 1.96, p=0.039) and local cases (AOR=11.18, 95% CI 7.43 to 16.83, p<0.001), and with significant improvement in the second wave compared with the first wave (average: 6.8 vs 3.7 days). A higher incidence rate was observed for males, raising possibility of gender predilection in susceptibility of developing symptoms.Conclusion: Prompt and stringent all-round containment strategies represent successful measures in pandemic control. These findings could inform formulation and implementation of pandemic mitigation strategies.
Journal of Medical Internet Research, Volume 22; https://doi.org/10.2196/20924

Abstract:
Background SARS-CoV-2, the novel coronavirus that causes COVID-19, is a global pandemic with higher mortality and morbidity than any other virus in the last 100 years. Without public health surveillance, policy makers cannot know where and how the disease is accelerating, decelerating, and shifting. Unfortunately, existing models of COVID-19 contagion rely on parameters such as the basic reproduction number and use static statistical methods that do not capture all the relevant dynamics needed for surveillance. Existing surveillance methods use data that are subject to significant measurement error and other contaminants. Objective The aim of this study is to provide a proof of concept of the creation of surveillance metrics that correct for measurement error and data contamination to determine when it is safe to ease pandemic restrictions. We applied state-of-the-art statistical modeling to existing internet data to derive the best available estimates of the state-level dynamics of COVID-19 infection in the United States. Methods Dynamic panel data (DPD) models were estimated with the Arellano-Bond estimator using the generalized method of moments. This statistical technique enables control of various deficiencies in a data set. The validity of the model and statistical technique was tested. Results A Wald chi-square test of the explanatory power of the statistical approach indicated that it is valid (χ210=1489.84, P<.001), and a Sargan chi-square test indicated that the model identification is valid (χ2946=935.52, P=.59). The 7-day persistence rate for the week of June 27 to July 3 was 0.5188 (P<.001), meaning that every 10,000 new cases in the prior week were associated with 5188 cases 7 days later. For the week of July 4 to 10, the 7-day persistence rate increased by 0.2691 (P=.003), indicating that every 10,000 new cases in the prior week were associated with 7879 new cases 7 days later. Applied to the reported number of cases, these results indicate an increase of almost 100 additional new cases per day per state for the week of July 4-10. This signifies an increase in the reproduction parameter in the contagion models and corroborates the hypothesis that economic reopening without applying best public health practices is associated with a resurgence of the pandemic. Conclusions DPD models successfully correct for measurement error and data contamination and are useful to derive surveillance metrics. The opening of America involves two certainties: the country will be COVID-19–free only when there is an effective vaccine, and the “social” end of the pandemic will occur before the “medical” end. Therefore, improved surveillance metrics are needed to inform leaders of how to open sections of the United States more safely. DPD models can inform this reopening in combination with the extraction of COVID-19 data from existing websites.
Vanessa Vasquez-Apestegui, Enrique Parras-Garrido, , Valeria M. Paz-Aparicio, Jhojan P. Rojas, Odón R. Sánchez-Ccoyllo,
Published: 6 July 2020
Abstract:
Background Corona virus disease (COVID-19) originated in China in December 2019. Thereafter, a global logarithmic expansion of the cases has occurred. Some countries have a higher rate of infections despite of early implementation of quarantine. Air pollution could be related to the high susceptibility to SARS-CoV-2 and the associated case-fatality rates (deaths/cases*100). Lima, Peru has the second highest incidence of COVID-19 in Latin America and it is also one of the cities with highest levels of air pollution in the Region. Methods This study investigated the association of the levels of PM2.5 exposure in the previous years (2010–2016) in 24 districts of Lima with the cases, deaths and case-fatality rates of COVID-19. Results Until June 12, 2020, there were 6,308 deaths and 220,749 SARS-CoV-2 positive cases in Peru. In Lima, the total number of COVID-19 deaths in all metropolitan areas was 2,382. The case-fatality rate at the national level was 2.58% and 1.93% in Lima. Higher PM2.5 levels are associated with higher number of cases and deaths of COVID-19. The case-fatality rate (Deaths/cases*100) did not increase with the increase in PM2.5 levels. A higher number of food markets was associated with higher incidence and mortality of COVID-19 (p< 0.01 for both); these associations persisted when cases (r = 0.49; p< 0.01) and deaths (r = 0.58; p< 0.01) were adjusted by the population density. The association of PM2.5 with cases of COVID-19 was maintained after controlling analysis by age, sex and number of food markers. Conclusions the higher rates of COVID-19 in Metropolitan Lima is attributable, among others, to the increased PM2.5 exposure in the previous years after adjusting for age, sex and number of food markets. Reduction of air pollution since a long term perspective, and social distancing are needed to prevent spreads of virus outbreak.
Kiana Shirani, , Zahra Torkpour, Mazyar Ghadiri Nejad, Bahareh Kamyab Moghadas, Matina Ghasemi, Hossein Akbari Aghdam, Athena Ehsani, Saeed Saber-Samandari,
Published: 1 July 2020
Abstract:
Nearly every 100 years, humans collectively face a pandemic crisis. After the Spanish flu, now the world is in the grip of coronavirus disease 2019 (COVID-19). First detected in 2019 in the Chinese city of Wuhan, COVID-19 causes severe acute respiratory distress syndrome. Despite the initial evidence indicating a zoonotic origin, the contagion is now known to primarily spread from person to person through respiratory droplets. The precautionary measures recommended by the scientific community to halt the fast transmission of the disease failed to prevent this contagious disease from becoming a pandemic for a whole host of reasons. After an incubation period of about two days to two weeks, a spectrum of clinical manifestations can be seen in individuals afflicted by COVID-19: from an asymptomatic condition that can spread the virus in the environment, to a mild/moderate disease with cold/flu-like symptoms, to deteriorated conditions that need hospitalization and intensive care unit management, and then a fatal respiratory distress syndrome that becomes refractory to oxygenation. Several diagnostic modalities have been advocated and evaluated; however, in some cases, diagnosis is made on the clinical picture in order not to lose time. A consensus on what constitutes special treatment for COVID-19 has yet to emerge. Alongside conservative and supportive care, some potential drugs have been recommended and a considerable number of investigations are ongoing in this regard
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