Predicting the issuance of COVID-19 stay-at-home orders in Africa: Using machine learning to develop insight for health policy research

Mansell, Jordan; Rhea, Carter; Murray, Gregg R.

doi:10.1016/j.ijdrr.2023.103598

Cited by 2 publications

(2 citation statements)

References 39 publications

(51 reference statements)

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“…Prior research documents the myriad ways in which COVID-19 has affected the lives of individuals around the world in areas such as government stay-at-home orders, health care, supply chains, inflation, school closures, and mental health (Carr et al, 2022; Fong et al, 2020; Jung et al, 2020; Kirkpatrick et al, 2020; Long et al, 2022; Mansell et al, 2023; Sajjad, 2021). Emerging scholarship also examines the effect that the COVID-19 pandemic has had on state security regarding health care identification and response systems, human security, and the preparedness of military forces (Albert et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

The effects of COVID-19 on domestic and international security in democratic and authoritarian regimes

Topping,

Hosny,

Hunter

et al. 2023

Polit. life sci.

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While numerous studies have examined how the COVID-19 pandemic has affected health care systems, supply chains, and economies, we do not understand how the pandemic has impacted the security of democratic and authoritarian states from a global standpoint. Thus, this study examines how COVID-19 has affected the security of democratic and authoritarian regimes. In conducting a historical, qualitative review of the security effects of the pandemic, we find that COVID-19 significantly affected domestic and international security for democratic and authoritarian states in both similar and varied ways. Additionally, the manner in which states responded to the pandemic was often conditioned by their regime type and by the nature of the governing leadership during the pandemic. These findings have important implications in considering how COVID-19 affected the security of democratic and authoritarian states, how regime type shapes government responses to infectious disease outbreaks, and how democratic and authoritarian states may respond to future pandemics.

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Section: Introductionmentioning

confidence: 99%

The effects of COVID-19 on domestic and international security in democratic and authoritarian regimes

Topping,

Hosny,

Hunter

et al. 2023

Polit. life sci.

View full text Add to dashboard Cite

show abstract

“…An increasing number of healthcare researchers and practitioners are leveraging quantitative methodologies to improve decision-making. Optimization and machine learning (ML) models have proven to be extremely helpful in medical decision-making and health policy 1 . However, such advanced decision-making methods can lead to inequitable outcomes as they do not give sufficient attention to underrepresented groups 2 , including those who are racially minoritized, low-income, or living in rural areas.…”

mentioning

confidence: 99%

A Survey on Optimization and Machine Learning-Based Fair Decision Making in Healthcare

Chen,

Marrero

2024

Preprint

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Background. Unintended biases introduced by optimization and machine learning (ML) models are of great interest to medical professionals. Bias in healthcare decisions can cause patients from vulnerable populations (e.g., racially minoritized, low-income) to have lower access to resources, exacerbating societal unfairness. Purpose. This review aims to identify, describe, and categorize literature regarding bias types, fairness metrics, and bias mitigation methods in healthcare decision making. Data Sources. Google Scholar database was searched to identify published studies. Study Selection. Eligible studies were required to present 1) types of bias 2) fairness metrics and 3) bias mitigation methods within decision-making in healthcare. Data Extraction. Studies were classified according to the three themes mentioned in the Study Selection. Information was extracted concerning the definitions, examples, applications, and limitations of bias types, fairness metrics, and bias mitigation methods. Data Synthesis. In bias type section, we included studies (n=15) concerning different biases. In the fairness metric section, we included studies (n=6) regarding common fairness metrics. In bias mitigation method section, themes included pre-processing methods (n=5), in-processing methods (n=16), and post-processing methods (n=4). Limitations. Most examples in our survey are from the United States since the majority of studies included in this survey were conducted in the United States. In the meanwhile, we limited the search language to English, so we may not capture some meaningful articles in other languages. Conclusions. Several types of bias, fairness metrics, and bias mitigation methods (especially optimization and machine learning-based methods) were identified in this review, with common themes based on analytical approaches. We also found topics such as explainability, fairness metric selection, and integration of prediction and optimization are promising directions for future studies.

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Predicting the issuance of COVID-19 stay-at-home orders in Africa: Using machine learning to develop insight for health policy research

Cited by 2 publications

References 39 publications

The effects of COVID-19 on domestic and international security in democratic and authoritarian regimes

The effects of COVID-19 on domestic and international security in democratic and authoritarian regimes

A Survey on Optimization and Machine Learning-Based Fair Decision Making in Healthcare

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