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Overview

MeSH Disease

Human Phenotype

Falls (1)


Transmission

Seroprevalence

There are no seroprevalence terms in the subcorpus

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    Optimal periodic closure for minimizing risk in emerging disease MESHD outbreaks

    Authors: Jason Hindes; Simone Bianco; Ira B. Schwartz

    id:2007.16151v1 Date: 2020-07-31 Source: arXiv

    Without vaccines and treatments, societies must rely on non-pharmaceutical intervention strategies to control the spread of emerging diseases such as COVID-19. Though complete lockdown is epidemiologically effective, because it eliminates infectious contacts, it comes with significant costs. Several recent studies have suggested that a plausible compromise strategy for minimizing epidemic risk is periodic closure, in which populations oscillate between wide-spread social restrictions and relaxation. However, no underlying theory has been proposed to predict and explain optimal closure periods as a function of epidemiological and social parameters. In this work we develop such an analytical theory for SEIR-like model diseases, showing how characteristic closure periods emerge that minimize the total outbreak, and increase predictably with the reproductive number TRANS and incubation periods TRANS of a disease, as long as both are within predictable limits. Using our approach we demonstrate a sweet-spot effect in which optimal periodic closure is maximally effective for diseases with similar incubation and recovery periods. Our results compare well to numerical simulations, including in COVID-19 models where infectivity and recovery show significant variability.

    Estimating transmission TRANS dynamics and serial interval TRANS of the first wave of COVID-19 infections under different control measures: A statistical analysis in Tunisia from February 29 to May 5, 2020

    Authors: Khouloud Talmoudi; Mouna Safer; Hejer Letaief; Aicha Hchaichi; Chahida Harizi; Sonia Dhaouadi; Sondes Derouiche; Ilhem Bouaziz; Donia Gharbi; Nourhene Najar; Molka Osman; Ines Cherif; Rym Mlallekh; Oumaima Ben-Ayed; Yosr Ayedi; Leila Bouabid; Souha Bougatef; Nissaf Bouafif ép Ben-Alaya; Mohamed Kouni Chahed

    doi:10.21203/rs.3.rs-31349/v1 Date: 2020-05-26 Source: ResearchSquare

    Background Describing transmission TRANS dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval TRANS (SI) and temporal reproduction number TRANS (Rt) of SARS-CoV-2 in Tunisia.Methods We collected data of investigations and contact tracing TRANS between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29-May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia MESHD. Maximum likelihood (ML) approach is used to estimate dynamics of Rt.Results 491 of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission TRANS. SI follows Gamma distribution with mean 5.30 days [95% CI 4.66–5.95] and standard deviation 0.26 [95% CI 0.23–0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% CI 2.73–3.69] to 1.77 [95% CI 1.49–2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CI 0.84–0.94]) by national lockdown measure.Conclusions Overall, our findings highlight contribution of interventions to interrupt transmission TRANS of SARS-CoV-2 in Tunisia.

    Estimating transmission TRANS dynamics and serial interval TRANS of the first wave of COVID-19 infections under different control measures: A statistical analysis in Tunisia from February 29 to May 5, 2020

    Authors: Khouloud Talmoudi; Mouna Safer; Hejer Letaief; Aicha Hchaichi; Chahida Harizi; Sonia Dhaouadi; Sondes Derouiche; Ilhem Bouaziz; Donia Gharbi; Nourhene Najar; Molka Osman; Ines Cherif; Rym Mlallekh; Oumaima Ben-Ayed; Yosr Ayedi; Leila Bouabid; Souha Bougatef; Nissaf Bouafif ép Ben-Alaya; Mohamed Kouni Chahed

    doi:10.21203/rs.3.rs-31349/v2 Date: 2020-05-26 Source: ResearchSquare

    Background: Describing transmission TRANS dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval TRANS (SI) and temporal reproduction number TRANS (Rt) of SARS-CoV-2 in Tunisia. Methods: We collected data of investigations and contact tracing TRANS between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29-May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia MESHD. Maximum likelihood (ML) approach is used to estimate dynamics of Rt. Results: 491 of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission TRANS. SI follows Gamma distribution with mean 5.30 days [95% CI 4.66-5.95] and standard deviation 0.26 [95% CI 0.23-0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% CI 2.73-3.69] to 1.77 [95% CI 1.49-2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CI 0.84-0.94]) by national lockdown measure.Conclusions: Overall, our findings highlight contribution of interventions to interrupt transmission TRANS of SARS-CoV-2 in Tunisia. 

    Modeling and Forecasting Trend of COVID-19 Epidemic in Iran

    Authors: Ali Ahmadi; Yasin Fadaei; Majid Shirani; Fereydoon Rahmani

    doi:10.1101/2020.03.17.20037671 Date: 2020-03-20 Source: medRxiv

    Background: COVID-19 is an emerging disease MESHD and precise data are not available in the world and Iran. this study aimed to determine the epidemic trend and prediction of COVID-19 in Iran. Methods: This study is a secondary data analysis and modeling. We used the daily reports of definitive COVID-19 patients (sampling of severe cases and hospitalization) released by Iran Ministry of Health and Medical Education. Epidemic projection models of Gompertz, Von Bertalanffy and least squared error MESHD (LSE) were used to predict the number of cases at April 3, 2020 until May 13, 2020. Results: R0 TRANS in Iran was estimated to be 4.7 that has now fallen HP to below 2. Given the three different scenarios, the prediction of the patients on April 3, 2020 by Von Bertalanffy, Gompertz and LSE were estimated at 48200, 52500 and 58000, respectively. The number of deceased COVID-19 patients was also estimated to be 3600 individuals using the Von growth model, 4200 ones by Gompertz's model and 4850 ones according to the LSE method. To predict and estimate the number of patients and deaths MESHD in the end of epidemic based on Von and Gompertz models, we will have 87000 cases, 4900 and 11000 deaths until 13 May and 1 June, respectively. Conclusion: The process of controlling the epidemic is tangible. If enforcement and public behavior interventions continue with current trends, the control and reduction of the COVID-19 epidemic in Iran will be flat from April 28, until July, 2020 and new cases are expected to decline from the following Iranian new year.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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