COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications

Rowland, Michael; Swannack, Todd M.; Mayo, Michael L.; Parno, Matthew; Farthing, Matthew W.; Dettwiller, Ian D.; George, Glover E.; England, William P.; Reif, Molly; Cegan, Jeffrey C.; Trump, Benjamin D.; Linkov, Igor; Lafferty, Brandon J.; Bridges, Todd S.

doi:10.21203/rs.3.rs-75665/v1

Cited by 4 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(the illness caused by SARS-CoV-2) worldwide with total documented deaths having exceeded one million at the end of September 2020 (WHO 2020b). Complicating the pandemic, codisruptions such as political unrest and evacuations due to the 2020 storm season decrease the potential to mitigate waves of illness outbreak (Rowland et al 2020). As cases, death tolls, and socioeconomic consequences rise, finding a viable vaccine is increasingly urgent.…”

Section: Introductionmentioning

confidence: 99%

The Vaccine Supply Chain: A Call for Resilience Analytics to Support COVID-19 Vaccine Production and Distribution

Golan

Trump

Cegan

et al. 2021

Risk, Systems and Decisions

Self Cite

View full text Add to dashboard Cite

Pharmaceutical companies, upstream suppliers, associated logistics providers, health workers, regulatory agencies, public health experts and ultimately the medical practitioners and general public have been navigating an increasingly globalized vaccine supply chain; any disruption to the supply chain may cause cascading failure and have devastating consequences. The COVID-19 pandemic has already highlighted the lack of resilience in supply chains, as global networks fail from disruptions at single nodes and connections. As the race for a COVID-19 vaccine continues, the importance of not only an efficient supply chain but a resilient vaccine supply chain capable of reliable production and reaching target populations despite likely but currently unknown disruptions is imperative. Proactively applying resilience analytics to vaccine supply chain models will increase the probability that vaccination programs meet their goals. Without such a network in place for manufacture and distribution of the COVID-19 vaccine, even the most efficacious and safe vaccine will not prove viable.

show abstract

Section: Introductionmentioning

confidence: 99%

The Vaccine Supply Chain: A Call for Resilience Analytics to Support COVID-19 Vaccine Production and Distribution

Golan

Trump

Cegan

et al. 2021

Risk, Systems and Decisions

Self Cite

View full text Add to dashboard Cite

show abstract

“…We also conducted an analysis on papers written by authors that contributed to the COVID-19 Forecast Hub, which includes 17 preprints 138–140,142–150,152–156 and 3 published papers 137,141,151 . 70% of these papers made short-term predictions and 40% of these papers made long-term predictions.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, papers from this journal were significantly less likely to state limitations (23% versus 64% for all papers). We also conducted an analysis on papers written by authors that contributed to the COVID-19 Forecast Hub, which includes 17 preprints [138][139][140][142][143][144][145][146][147][148][149][150][152][153][154][155][156] and 3 published papers 137,141,151 . 70% of these papers made short-term predictions and 40% of these papers made long-term predictions.…”

Section: Figure 4 Top 10 Journals In the Final Setmentioning

confidence: 99%

An Evaluation of Prospective COVID-19 Modeling: From Data to Science Translation

Nixon

Jindal

Parker

et al. 2022

Preprint

View full text Add to dashboard Cite

SummaryBackgroundInfectious disease modeling can serve as a powerful tool for science-based management of outbreaks, providing situational awareness and decision support for policy makers. Predictive modeling of an emerging disease is challenging due to limited knowledge on its epidemiological characteristics. For COVID-19, the prediction difficulty was further compounded by continuously changing policies, varying behavioral responses, poor availability and quality of crucial datasets, and the variable influence of different factors as the pandemic progresses. Due to these challenges, predictive modeling for COVID-19 has earned a mixed track record.MethodsWe provide a systematic review of prospective, data-driven modeling studies on population-level dynamics of COVID-19 in the US and conduct a quantitative assessment on crucial elements of modeling, with a focus on the aspects of modeling that are critical to make them useful for decision-makers. For each study, we documented the forecasting window, methodology, prediction target, datasets used, geographic resolution, whether they expressed quantitative uncertainty, the type of performance evaluation, and stated limitations. We present statistics for each category and discuss their distribution across the set of studies considered. We also address differences in these model features based on fields of study.FindingsOur initial search yielded 2,420 papers, of which 119 published papers and 17 preprints were included after screening. The most common datasets relied upon for COVID-19 modeling were counts of cases (93%) and deaths (62%), followed by mobility (26%), demographics (25%), hospitalizations (12%), and policy (12%). Our set of papers contained a roughly equal number of short-term (46%) and long-term (60%) predictions (defined as a prediction horizon longer than 4 weeks) and statistical (43%) versus compartmental (47%) methodologies. The target variables used were predominantly cases (89%), deaths (52%), hospitalizations (10%), and Rt (9%). We found that half of the papers in our analysis did not express quantitative uncertainty (50%). Among short-term prediction models, which can be fairly evaluated against truth data, 25% did not conduct any performance evaluation, and most papers were not evaluated over a timespan that includes varying epidemiological dynamics. The main categories of limitations stated by authors were disregarded factors (39%), data quality (28%), unknowable factors (26%), limitations specific to the methods used (22%), data availability (16%), and limited generalizability (8%). 36% of papers did not list any limitations in their discussion or conclusion section.InterpretationPublished COVID-19 models were found to be consistently lacking in some of the most important elements required for usability and translation, namely transparency, expressing uncertainty, performance evaluation, stating limitations, and communicating appropriate interpretations. Adopting the EPIFORGE 2020 guidelines would address these shortcomings and improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. We also discovered that most of the operational models that have been used in real-time to inform decision-making have not yet made it into the published literature, which highlights that the current publication system is not suited to the rapid information-sharing needs of outbreaks. Furthermore, data quality was identified to be one of the most important drivers of model performance, and a consistent limitation noted by the modeling community. The US public health infrastructure was not equipped to provide timely, high-quality COVID-19 data, which is required for effective modeling. Thus, a systematic infrastructure for improved data collection and sharing should be a major area of investment to support future pandemic preparedness.

show abstract

“…These practices have been proven effective. The lockdown policy in the United States has effectively reduced the COVID-19 death rate (20)(21)(22)(23). Places where people often gather were severely restricted.…”

Section: Literature Reviewmentioning

confidence: 99%

A weapon to fight against pervasive Omicron: systematic actions transiting to pre-COVID normal

Wang,

Xue,

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

The Coronavirus Disease-2019 (COVID-19) pandemic is not just a health crisis but also a social crisis. Confronted with the resurgence of variants with massive infections, the triggered activities from personal needs may promote the spread, which should be considered in risk management. Meanwhile, it is important to ensure that the policy responses on citizen life to a lower level. In the face of Omicron mutations, we need to sum up the control experience accumulated, adapting strategies in the dynamic coevolution process while balancing life resumption and pandemic control, to meet challenges of future crises. We collected 46 cases occurring between 2021 and 2022, mainly from China, but also including five relevant cases from other countries around the world. Based on case studies, we combine micro-view individual needs/behaviors with macro-view management measures linking Maslow’s hierarchy of needs with the transmission chain of Omicron clusters. The proposed loophole chain could help identify both individual and management loopholes in the spread of the virus. The systematic actions that were taken have effectively combated these ubiquitous vulnerabilities at lower costs and lesser time. In the dynamic coevolution process, the Chinese government has made effective and more socially acceptable prevention policies while meeting the divergent needs of the entire society at the minimum costs. Systematic actions do help maintain the balance between individuals’ satisfaction and pandemic containment. This implies that risk management policies should reasonably consider individual needs and improve the cooperation of various stakeholders with targeted flexible measures, securing both public health and life resumption.

show abstract

COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications

Cited by 4 publications

References 17 publications

The Vaccine Supply Chain: A Call for Resilience Analytics to Support COVID-19 Vaccine Production and Distribution

The Vaccine Supply Chain: A Call for Resilience Analytics to Support COVID-19 Vaccine Production and Distribution

An Evaluation of Prospective COVID-19 Modeling: From Data to Science Translation

A weapon to fight against pervasive Omicron: systematic actions transiting to pre-COVID normal

Contact Info

Product

Resources

About