What is global health? Key concepts and clarification of misperceptions

Chen, Xinguang; Li, Hao; Lucero‐Prisno, Don Eliseo; Abdullah, Abu S.; Huang, Jiayan; Laurence, Charlotte; Liang, Xiaohui; Ma, Zhenyu; Mao, Zongfu; Ren, Rong; Wu, Shaolong; Wang, Nan; Wang, Peigang; Wang, Tingting; Yan, Hong; Zou, Yuliang

doi:10.1186/s41256-020-00142-7

Cited by 49 publications

(43 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Understanding and curbing the COVID-19 epidemic in the U.S. is an essential part of fighting the pandemic globally. 1 This study provides data important for informing public health decision-making designed to end the epidemic in the U.S. Our study also demonstrates the utility and efficiency of the 5-parameter logistic growth model for examining the dynamics of an epidemic in its early period when little data is available. Additionally, our selection of the 5parameter logistic exponential growth model was based on intensive testing of other models, including 2-parameter, 3-parameter, and 4-parameter models.…”

Section: Resultsmentioning

confidence: 72%

“…Spreading worldwide in less than five months, the COVID-19 pandemic is a typical example of a global health issue. 1 In the months since the first COVID-19 case was reported in the United States on January 22, 2020, many studies have employed different models to reconstruct the epidemic (i.e., the spread of COVID-19 within the United States only) and forecast its future trends, from simple growth models to classic susceptible-infected-recovered models. 2 Yet due to the scarcity of available information about the early period of the COVID-19 epidemic, researchers lack sufficient data to construct complex and classic epidemiological models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reconstructing and Forecasting the COVID-19 Epidemic in the US Using a 5-Parameter Logistic Growth Model

Chen

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Many studies have modeled and predicted the spread of COVID-19 (coronavirus disease 2019) in the U.S. using data that begins with the first reported cases. However, the shortage of testing services to detect infected persons makes this approach subject to error due to its underdetection of early cases in the U.S. Our new approach overcomes this limitation and provides data supporting the public policy decisions intended to combat the spread of COVID-19 epidemic. Methods: We used Centers for Disease Control and Prevention data documenting the daily new and cumulative cases of confirmed COVID-19 in the U.S. from January 22 to April 6, 2020, and reconstructed the epidemic using a 5-parameter logistic growth model. We fitted our model to data from a 2-week window (i.e., from March 21 to April 4, approximately one incubation period) during which large-scale testing was being conducted. With parameters obtained from this modeling, we reconstructed and predicted the growth of the epidemic and evaluated the extent and potential effects of underdetection. Results: The data fit the model satisfactorily. The estimated daily growth rate was 16.8% overall with 95% CI: [15.95%, 17.76%], suggesting a doubling period of 4 days. Based on the modeling result, the tipping point at which new cases will begin to decline will be on April 7th, 2020, with a peak of 32,860 new cases on that day. By the end of the epidemic, at least 792,548 (95% CI: [789,162, 795,934]) will be infected in the U.S. Based on our model, a total of 12,029 cases were not detected between January 22 (when the first case was detected in the U.S.) and April 4. Conclusions: Our findings demonstrate the utility of a 5-parameter logistic growth model with reliable data that comes from a specified period during which governmental interventions were appropriately implemented. Beyond informing public health decision-making, our model adds a tool for more faithfully capturing the spread of the COVID-19 epidemic.

show abstract

Section: Resultsmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Reconstructing and Forecasting the COVID-19 Epidemic in the US Using a 5-Parameter Logistic Growth Model

Chen

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Understanding and curbing the COVID-19 epidemic in the U.S. is an essential part of fighting the pandemic globally [1]. This study provides data important for informing public health decision-making designed to end the epidemic in the U.S. Our study also demonstrates the utility and efficiency of the 5-parameter logistic growth model for examining the dynamics of an epidemic in its early period when little data is available.…”

Section: Resultsmentioning

confidence: 83%

“…Coronavirus disease 2019 (COVID-19) is an infection caused by a novel pathogen named SARS-Cov-2. Spreading worldwide in less than five months, the COVID-19 pandemic is a typical example of a global health issue [1]. In the months since the first COVID-19 case was reported in the United States on January 22, 2020, many studies have employed different models to reconstruct the epidemic (i.e., the spread of COVID-19 within the United States only) and forecast its future trends, from simple growth models to classic susceptible-infectedrecovered models [2].…”

Section: Introductionmentioning

confidence: 99%

Reconstructing and forecasting the COVID-19 epidemic in the United States using a 5-parameter logistic growth model

Chen

2020

glob health res policy

Self Cite

View full text Add to dashboard Cite

Background: Many studies have modeled and predicted the spread of COVID-19 (coronavirus disease 2019) in the U.S. using data that begins with the first reported cases. However, the shortage of testing services to detect infected persons makes this approach subject to error due to its underdetection of early cases in the U.S. Our new approach overcomes this limitation and provides data supporting the public policy decisions intended to combat the spread of COVID-19 epidemic. Methods: We used Centers for Disease Control and Prevention data documenting the daily new and cumulative cases of confirmed COVID-19 in the U.S. from January 22 to April 6, 2020, and reconstructed the epidemic using a 5-parameter logistic growth model. We fitted our model to data from a 2-week window (i.e., from March 21 to April 4, approximately one incubation period) during which large-scale testing was being conducted. With parameters obtained from this modeling, we reconstructed and predicted the growth of the epidemic and evaluated the extent and potential effects of underdetection. Results: The data fit the model satisfactorily. The estimated daily growth rate was 16.8% overall with 95% CI: [15.95, 17.76%], suggesting a doubling period of 4 days. Based on the modeling result, the tipping point at which new cases will begin to decline will be on April 7th, 2020, with a peak of 32,860 new cases on that day. By the end of the epidemic, at least 792,548 (95% CI: [789,162, 795,934]) will be infected in the U.S. Based on our model, a total of 12,029 cases were not detected between January 22 (when the first case was detected in the U.S.) and April 4. Conclusions: Our findings demonstrate the utility of a 5-parameter logistic growth model with reliable data that comes from a specified period during which governmental interventions were appropriately implemented. Beyond informing public health decision-making, our model adds a tool for more faithfully capturing the spread of the COVID-19 epidemic.

show abstract

“…It may greatly increase the e ciency to solve many global health issues if we approach these issues with a globally focused perspective. (8) This study, therefore, establishes the impacts and lessons learned from local production of WHO-recommended alcohol-based hand rub during the Covid-19 Pandemic.…”

Section: Introductionmentioning

confidence: 71%

Impacts and lessons learned from the local production of WHO-Recommended Alcohol-Based Hand Rub during Covid-19 Pandemic in Uganda.

Tusabe

Otita

Kesande

et al. 2020

Preprint

View full text Add to dashboard Cite

In January 2020, the World Health Organization (WHO) declared the outbreak of a new coronavirus disease, COVID-19. The WHO and the Centers for Disease Control and Prevention (CDC) recommend using an alcohol-based hand rub (ABHR) as one of the measures to combat the spread of COVID-19. Objective To assess the impacts and lessons learned from the local production of WHO-recommended alcohol-based hand rub during the COVID-19 Pandemic in Uganda. Method Space for local production was identified at a health facility in Central Uganda that was air-conditioned and well ventilated. Volunteers were trained on the local production of ABHR. The volunteers were trained theoretically and practically for Four days; and each was able to produce at least a batch of ABHR. Logistical support for supplying the raw materials needed for production was from the Infectious Diseases Institute. There were also already established ABHR production units and trained staff on local production of ABHR in 2019 in the Kabarole and Kasese districts, respectively in Western Uganda. These districts were threatened by several outbreaks, including Ebola virus disease, and were monitored for ABHR production during the COVID-19 Pandemic. Result Two production units were set up during the Ebola outbreak, in awake of COVID-19, a production unit in Kasese, was able to produce 3020 litres, and the Kabarole unit produced 2020 litres respectively. A new production unit was set up at Kasangati HCIV in central Uganda and eight laboratory technologists trained on Local Production of ABHR, and within Eleven days they were able to produce 2300ltrs. Conclusion The initiative to produce WHO-recommended hand rub locally during an outbreak is feasible, especially in a resource-limited country like Uganda. There is a need to rapidly scale-up the production capacity of ABHR and to ensure and maintain quality assurance at each production unit.

show abstract

What is global health? Key concepts and clarification of misperceptions

Cited by 49 publications

References 68 publications

Reconstructing and Forecasting the COVID-19 Epidemic in the US Using a 5-Parameter Logistic Growth Model

Reconstructing and Forecasting the COVID-19 Epidemic in the US Using a 5-Parameter Logistic Growth Model

Reconstructing and forecasting the COVID-19 epidemic in the United States using a 5-parameter logistic growth model

Impacts and lessons learned from the local production of WHO-Recommended Alcohol-Based Hand Rub during Covid-19 Pandemic in Uganda.

Contact Info

Product

Resources

About