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      Human mobility and poverty as key drivers of COVID-19 transmission and control

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          Abstract

          Background

          Applying heavy nationwide restrictions is a powerful method to curtail COVID-19 transmission but poses a significant humanitarian and economic crisis. Thus, it is essential to improve our understanding of COVID-19 transmission, and develop more focused and effective strategies. As human mobility drives transmission, data from cellphone devices can be utilized to achieve these goals.

          Methods

          We analyzed aggregated and anonymized mobility data from the cell phone devices of> 3 million users between February 1, 2020, to May 16, 2020 — in which several movement restrictions were applied and lifted in Israel. We integrated these mobility patterns into age-, risk- and region-structured transmission model. Calibrated to coronavirus incidence in 250 regions covering Israel, we evaluated the efficacy and effectiveness in decreasing morbidity and mortality of applying localized and temporal lockdowns (stay-at-home order).

          Results

          Poorer regions exhibited lower and slower compliance with the restrictions. Our transmission model further indicated that individuals from impoverished areas were associated with high transmission rates. Considering a horizon of 1–3 years, we found that to reduce COVID-19 mortality, school closure has an adverse effect, while interventions focusing on the elderly are the most efficient. We also found that applying localized and temporal lockdowns during regional outbreaks reduces the overall mortality and morbidity compared to nationwide lockdowns. These trends were consistent across vast ranges of epidemiological parameters, and potential seasonal forcing.

          Conclusions

          More resources should be devoted to helping impoverished regions. Utilizing cellphone data despite being anonymized and aggregated can help policymakers worldwide identify hotspots and apply designated strategies against future COVID-19 outbreaks.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12889-021-10561-x.

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          Most cited references29

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          Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

          Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/mL (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.
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            The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application

            Background: A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities. Objective: To estimate the length of the incubation period of COVID-19 and describe its public health implications. Design: Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020. Setting: News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China. Participants: Persons with confirmed SARS-CoV-2 infection outside Hubei province, China. Measurements: Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization. Results: There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine. Limitation: Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases. Conclusion: This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases. Primary Funding Source: U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.
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              SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients

              To the Editor: The 2019 novel coronavirus (SARS-CoV-2) epidemic, which was first reported in December 2019 in Wuhan, China, and has been declared a public health emergency of international concern by the World Health Organization, may progress to a pandemic associated with substantial morbidity and mortality. SARS-CoV-2 is genetically related to SARS-CoV, which caused a global epidemic with 8096 confirmed cases in more than 25 countries in 2002–2003. 1 The epidemic of SARS-CoV was successfully contained through public health interventions, including case detection and isolation. Transmission of SARS-CoV occurred mainly after days of illness 2 and was associated with modest viral loads in the respiratory tract early in the illness, with viral loads peaking approximately 10 days after symptom onset. 3 We monitored SARS-CoV-2 viral loads in upper respiratory specimens obtained from 18 patients (9 men and 9 women; median age, 59 years; range, 26 to 76) in Zhuhai, Guangdong, China, including 4 patients with secondary infections (1 of whom never had symptoms) within two family clusters (Table S1 in the Supplementary Appendix, available with the full text of this letter at NEJM.org). The patient who never had symptoms was a close contact of a patient with a known case and was therefore monitored. A total of 72 nasal swabs (sampled from the mid-turbinate and nasopharynx) (Figure 1A) and 72 throat swabs (Figure 1B) were analyzed, with 1 to 9 sequential samples obtained from each patient. Polyester flock swabs were used for all the patients. From January 7 through January 26, 2020, a total of 14 patients who had recently returned from Wuhan and had fever (≥37.3°C) received a diagnosis of Covid-19 (the illness caused by SARS-CoV-2) by means of reverse-transcriptase–polymerase-chain-reaction assay with primers and probes targeting the N and Orf1b genes of SARS-CoV-2; the assay was developed by the Chinese Center for Disease Control and Prevention. Samples were tested at the Guangdong Provincial Center for Disease Control and Prevention. Thirteen of 14 patients with imported cases had evidence of pneumonia on computed tomography (CT). None of them had visited the Huanan Seafood Wholesale Market in Wuhan within 14 days before symptom onset. Patients E, I, and P required admission to intensive care units, whereas the others had mild-to-moderate illness. Secondary infections were detected in close contacts of Patients E, I, and P. Patient E worked in Wuhan and visited his wife (Patient L), mother (Patient D), and a friend (Patient Z) in Zhuhai on January 17. Symptoms developed in Patients L and D on January 20 and January 22, respectively, with viral RNA detected in their nasal and throat swabs soon after symptom onset. Patient Z reported no clinical symptoms, but his nasal swabs (cycle threshold [Ct] values, 22 to 28) and throat swabs (Ct values, 30 to 32) tested positive on days 7, 10, and 11 after contact. A CT scan of Patient Z that was obtained on February 6 was unremarkable. Patients I and P lived in Wuhan and visited their daughter (Patient H) in Zhuhai on January 11 when their symptoms first developed. Fever developed in Patient H on January 17, with viral RNA detected in nasal and throat swabs on day 1 after symptom onset. We analyzed the viral load in nasal and throat swabs obtained from the 17 symptomatic patients in relation to day of onset of any symptoms (Figure 1C). Higher viral loads (inversely related to Ct value) were detected soon after symptom onset, with higher viral loads detected in the nose than in the throat. Our analysis suggests that the viral nucleic acid shedding pattern of patients infected with SARS-CoV-2 resembles that of patients with influenza 4 and appears different from that seen in patients infected with SARS-CoV. 3 The viral load that was detected in the asymptomatic patient was similar to that in the symptomatic patients, which suggests the transmission potential of asymptomatic or minimally symptomatic patients. These findings are in concordance with reports that transmission may occur early in the course of infection 5 and suggest that case detection and isolation may require strategies different from those required for the control of SARS-CoV. How SARS-CoV-2 viral load correlates with culturable virus needs to be determined. Identification of patients with few or no symptoms and with modest levels of detectable viral RNA in the oropharynx for at least 5 days suggests that we need better data to determine transmission dynamics and inform our screening practices.
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                Author and article information

                Contributors
                dan.yamin@gmail.com
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                25 March 2021
                25 March 2021
                2021
                : 21
                : 596
                Affiliations
                [1 ]GRID grid.12136.37, ISNI 0000 0004 1937 0546, Laboratory for Epidemic Modeling and Analysis, Department of Industrial Engineering, , Faculty of Engineering, Tel Aviv University, ; 6997801 Tel Aviv, Israel
                [2 ]GRID grid.12136.37, ISNI 0000 0004 1937 0546, Center for Combatting Pandemics, , Tel Aviv University, ; 6997801 Tel Aviv, Israel
                Author information
                http://orcid.org/0000-0002-1131-5572
                Article
                10561
                10.1186/s12889-021-10561-x
                7993906
                33388037
                d690b94a-e85e-4428-b539-c1f6a2324710
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 9 July 2020
                : 4 March 2021
                Funding
                Funded by: Israel Science Foundation
                Award ID: 3409/19
                Award Recipient :
                Funded by: The Zimin Institute for Engineering Solutions Advancing Better Lives
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Public health
                contact mixing patterns,human mobility,covid-19,transmission model,sir model
                Public health
                contact mixing patterns, human mobility, covid-19, transmission model, sir model

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