Evidence map›Paper›PMID 33580512›Full record

ReviewRisk analysis : an official publication of the Society for Risk Analysis2022

Optimal Investment in Prevention and Recovery for Mitigating Epidemic Risks.

C Derrick Huang, Milad Baghersad, Ravi S Behara, Christopher W Zobel

Open access · bronzeAbstract readReview
In one paragraph

Review in Risk analysis : an official publication of the Society for Risk Analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 11 citations in OpenAlex.

  1. Risk modelling of ESG (environmental, social, and governance), healthcare, and financial sectors.Risk analysis : an official publication of the Society for Risk Analysis · 2025
    Article
  2. Global systemic risk and resilience for novel coronavirus in postpandemic era.Risk analysis : an official publication of the Society for Risk Analysis · 2022
    Article
  3. Global Systemic Risk and Resilience for Novel Coronavirus and COVID-19.Risk analysis : an official publication of the Society for Risk Analysis · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

C Derrick HuangDepartment of Information Technology & Operations Management, College of Business, Florida Atlantic University, Boca Raton, FL, USA.ORCID 0000-0003-1551-3477
Milad BaghersadDepartment of Information Technology & Operations Management, College of Business, Florida Atlantic University, Boca Raton, FL, USA.ORCID 0000-0002-2748-9577
Ravi S BeharaDepartment of Information Technology & Operations Management, College of Business, Florida Atlantic University, Boca Raton, FL, USA.ORCID 0000-0001-5078-5377
Christopher W ZobelDepartment of Business Information Technology, Pamplin College of Business, Virginia Tech, Blacksburg, VA, USA.ORCID 0000-0002-0952-7322
Florida Atlantic University · USVirginia Tech · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The worldwide healthcare and economic crisis caused by the COVID-19 pandemic highlights the need for a deeper understanding of investing in the mitigation of epidemic risks. To address this, we built a mathematical model to optimize investments into two types of measures for mitigating the risks of epidemic propagation: prevention/containment measures and treatment/recovery measures. The new model explicitly accounts for the characteristics of networks of individuals, as a critical element of epidemic propagation. Subsequent analysis shows that, to combat an epidemic that can cause significant negative impact, optimal investment in either category increases with a higher level of connectivity and intrinsic loss, but it is limited to a fraction of that total potential loss. However, when a fixed and limited mitigation investment is to be apportioned among the two types of measures, the optimal proportion of investment for prevention and containment increases when the investment limit goes up, and when the network connectivity decreases. Our results are consistent with existing studies and can be used to properly interpret what happened in past pandemics as well as to shed light on future and ongoing events such as COVID-19.

Indexed as

SARS-CoV-2COVID-19HumansPandemicsQuarantineCOVID-19epidemic risk mitigationoptimal investmentprevention and containmenttreatment and recovery

Identifiers

PMID33580512
PMCPMC8014190
OpenAlexW3126606792

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.