The 1918 Influenza Pandemic: Lessons Learned and Not—Introduction to the Special Section

Parmet, Wendy E.; Rothstein, Mark A.

doi:10.2105/ajph.2018.304695

Cited by 40 publications

(29 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…What factors inhibit prompt response and success during a pandemic? Let us assume that they include "distrust, isolationism and hubris" (Parmet and Rothstein, 2018). In our discussion it will become clear that all these factors mattered for performance in both 1918 and in 2020.…”

Section: Discussionmentioning

confidence: 99%

A Note on Long-Run Persistence of Public Health Outcomes in Pandemics

Lin¹,

Meissner²

2020

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

A Note on Long-Run Persistence of Public Health Outcomes in Pandemics

Lin¹,

Meissner²

2020

View full text Add to dashboard Cite

show abstract

“…The new coronavirus (COVID-19) has been recognized as the most serious threat to public health since the influenza pandemic of 1918 (Parmet and Rothstein 2018 ). It is a general assumption that density is correlated with higher transmission rates, infection levels and mortality from highly communicable diseases such as COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach

Arif

Sengupta

2020

Environ Dev Sustain

View full text Add to dashboard Cite

The unprecedented growth of the novel coronavirus (SARS-CoV-2) as a severe acute respiratory syndrome escalated to the coronavirus disease 2019 (COVID-19) pandemic. It has created an unanticipated global public health crisis that is spreading rapidly in India as well, posing a serious threat to 1350 million persons. Among the factors, population density is foremost in posing a challenge in controlling the COVID-19 contagion. In such extraordinary times, evidence-based knowledge is the prime requisite for pacifying the effect. In this piece, we have studied the district wise transmissions of the novel coronavirus in five south Indian states until 20th July 2020 and its relationship with their respective population density. The five states are purposefully selected for their records in better healthcare infrastructure vis-à-vis other states in India. The study uses Pearson’s correlation coefficient to account for the direct impact of population density on COVID-19 transmission rate. Response surface methodology approach is used to validate the correlation between density and transmission rate and spatiotemporal dynamics is highlighted using Thiessen polygon method. The analysis has found that COVID-19 transmission in four states (Kerala, Tamil Nadu, Karnataka and Telangana) strongly hinges upon the spatial distribution of population density. In addition, the results indicate that the long-term impacts of the COVID-19 crisis are likely to differ with demographic density. In conclusion, those at the helm of affairs must take cognizance of the vulnerability clusters together across districts.

show abstract

“…The 1918 pandemic has been estimated to have infected 500 million people (around one-third of the world's population of 1.8 billion at that time (Parmet & Rothstein, 2018)), with at least 50 million deaths worldwide (Centers for Disease Control and Prevention, 2019). This results in a CFR of approximately 10%.…”

Section: Introductionmentioning

confidence: 99%

Death tolls of COVID-19: Where come the fallacies and ways to make them more accurate

Lin

Wan

Huang

et al. 2020

Global Public Health

View full text Add to dashboard Cite

The death toll of the coronavirus disease 2019 (COVID-19) sparked much controversy since its advent in December 2019. Underestimation because of under testing and deaths happening outside the hospitals were important causes. Bold revisions of the diagnostic criteria leading to dramatic changes in death tolls by different governments were observed in attempts to generate more accurate estimates. On the other hand, the influence, censorship and manipulation on case and death data from top political leaders of some countries could create important impacts on the death toll. Baseline mortality data of previous years may help make more accurate estimates of the actual death toll. The pitfalls and strategies during such processes could become valuable lessons to leaders and policymakers worldwide as more accurate statistics serve to navigate policies to combat this pandemic in the days and months to come.

show abstract

The 1918 Influenza Pandemic: Lessons Learned and Not—Introduction to the Special Section

Cited by 40 publications

References 0 publications

A Note on Long-Run Persistence of Public Health Outcomes in Pandemics

A Note on Long-Run Persistence of Public Health Outcomes in Pandemics

Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach

Death tolls of COVID-19: Where come the fallacies and ways to make them more accurate

Contact Info

Product

Resources

About