The numerical solution of model (1) for infected compartment vs. time with different scenarios of Phase 2 in the First Case: 1^<i>st</i> scenario, no lockdown is implemented; 2^<i>nd</i> scenario, no lockdown while <i>SD</i> increases; 3^<i>rd</i> scenario, lockdown increases; 4^<i>th</i> scenario, <i>SD</i> increases; 5^<i>th</i> scenario, lockdown decreases; 6^<i>th</i> scenario, <i>SD</i> decreases; 7^<i>th</i> scenario, lockdown and <i>SD</i> increases; 8^<i>th</i> scenario, lockdown decreases and <i>SD</i> increases; 9^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 1; 10^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 1 while <i>SD</i> increases; 11^<i>th</i> scenario, <i>ρ</i> = 0.75 in all Phase 2; 12^<i>th</i> scenario, <i>ρ</i> = 0.75 while <i>SD</i> increases. by Sara K. Al-Harbi (12418482)

“…<p>The numerical solution of model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0265779#pone.0265779.e002" target="_blank">(1)</a> for infected compartment vs. time with different scenarios of Phase 2 in the First Case: 1^<i>st</i> scenario, no lockdown is implemented; 2^<i>nd</i> scenario, no lockdown while <i>SD</i> increases; 3^<i>rd</i> scenario, lockdown increases; 4^<i>th</i> scenario, <i>SD</i> increases; 5^<i>th</i> scenario, lockdown decreases; 6^<i>th</i> scenario, <i>SD</i> decreases; 7^<i>th</i> scenario, lockdown and <i>SD</i> increases; 8^<i>th</i> scenario, lockdown decreases and <i>SD</i> increases; 9^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 1; 10^<i>th</i> scenario, <i>ρ</i> is the same as in Phase 1 while <i>SD</i> increases; 11^<i>th</i> scenario, <i>ρ</i> = 0.75 in all Phase 2; 12^<i>th</i> scenario, <i>ρ</i> = 0.75 while <i>SD</i> increases.…”

Epidemic risk for the effective reproduction numbers (<i>R</i>_<i>e</i>) corresponding to reduced risk (1.1) and the minimum (1.4), median (2.8), and maximum (4.4) estima... by Emily M. Javan (15194865)

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Estimated risk of hospitalization and/or death among nonhospitalized adults with COVID-19 by age and comorbid conditions. by Scott Dryden-Peterson (227433)

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Daily COVID-19 cases in Islamabad and Peshawar pre- and post- school closure and school re-opening schools. by Abdul Mueed (12436636)

Monthly online long-COVID search volume and number of new COVID-19 cases reported in Japan from January 2020 to December 2022. by Kosuke Ishizuka (12889974)

“…<p>Online interest in long-COVID increased in mid-2021, early 2022, and late 2022, corresponding to peaks in the number of new cases of COVID-19 reported during waves of the COVID-19 epidemic in Japan. …”

Characteristics of the included studies. by Konstantinos S. Kechagias (11757239)

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PRISMA flowchart. by Konstantinos S. Kechagias (11757239)

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Routine laboratory findings of mild and severe COVID-19 patients. by Peerapong Kamjai (14185451)

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WHO Leprosy Indicators of Physical Disability Among Newly Detected Leprosy Cases in Yunnan, China, 1990–2020. by Xiaohua Chen (257762)

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Double-entry chart with analyzed samples (example of a real case). by Sofía Heckel (14059504)

Yearly count of reported cases of euthanasia by reason. by Jacques Wels (13837898)

An analysis of chronic kidney disease as a prognostic factor in pediatric cases of COVID-19 by Bárbara Caroline Dias Faria (10468144)

“…This narrative review aimed to look into whether CKD contributed to more severe involvement in pediatric patients with COVID-19. The studies included in this review did not report severe cases or deaths, and indicated that pediatric patients with CKD and previously healthy children recovered quickly from infection. …”

Number of cases found via active case finding (ACF) and passive case finding (PCF) in the IMPACT TB implementation districts in Nepal and Vietnam. by Diepreye Victoria Ayabina (11719488)

Increases in alert level and improved border measures resulted in decreases in secondary transmissions. by Alex James (2916851)

Descriptive statistics of endogenous and exogenous variables. by Weiyu Luo (4782711)

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Statistics of sub-models’ R-squared on dynamic panel datasets. by Weiyu Luo (4782711)

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Random effects of lockdown level on natural-logged three types of mobility metrics. by Weiyu Luo (4782711)

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A big-data-driven analytical framework of human mobility and pandemics. by Weiyu Luo (4782711)

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Date range when dynamic coefficients’ P-value < 0.05 during emergent stage of the mobility metrics. by Weiyu Luo (4782711)

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(a) illustration of 3 types of human mobility flow. by Weiyu Luo (4782711)

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