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REVIEW

COVID-19 Pandemic Risk Assessment: Systematic Review

ORCID Icon, , ORCID Icon & ORCID Icon
Pages 903-925 | Received 12 Oct 2023, Accepted 05 Jan 2024, Published online: 11 Apr 2024

References

  • Coronavirus disease 2019 (COVID-19) situation report—51; World Health Organization: Geneva, Switzerland; 2020. Available from: https://www.who.int/publications/m/item/situation-report---51. Accessed March 3, 2023.
  • So MKP, Chu AMY, Chan TWC. Impacts of the COVID-19 pandemic on financial market connectedness. Finance Res Lett. 2021;38:e101864. doi:10.1016/j.frl.2020.101864
  • Goodell JW, Goutte S. Co-movement of COVID-19 and Bitcoin: evidence from wavelet coherence analysis. Finance Res Lett. 2021;38:e101625. doi:10.1016/j.frl.2020.101625
  • Nicola M, Alsafi Z, Sohrabi C, et al. The socio-economic implications of the coronavirus pandemic (COVID-19): a review. Int j Surg. 2020;78:185–193. doi:10.1016/j.ijsu.2020.04.018
  • Zambrano-Monserrate MA, Ruano M, Sanchez-Alcalde L. Indirect effects of COVID-19 on the environment. Science of the Total Environment. 2020;728:e138813. doi:10.1016/j.scitotenv.2020.138813
  • Xiong J, Lipsitz O, Nasri F, et al. Impact of COVID-19 pandemic on mental health in the general population: a systematic review. J Affective Disorders. 2020;277:55–64. doi:10.1016/j.jad.2020.08.001
  • Nsoesie EO, Cesare N, Müller M, Ozonoff A. COVID-19 misinformation spread in eight countries: exponential growth modeling study. J Med Internet Res. 2020;22(12):e24425. doi:10.2196/24425
  • Lee JJ, Kang K, Wang MP, et al. Associations between COVID-19 misinformation exposure and belief with COVID-19 knowledge and preventive behaviors: cross-sectional online study. J Med Internet Res. 2020;22(11):e22205. doi:10.2196/22205
  • Lundberg AL, Lorenzo-Redondo R, Hultquist JF, et al. Overlapping Delta and Omicron outbreaks during the COVID-19 pandemic: dynamic panel data estimates. JMIR Public Health Surveillance. 2022;8(6):e37377. doi:10.2196/37377
  • Chandrasekaran R, Mehta V, Valkunde T, Moustakas E. Topics, trends, and sentiments of Tweets about the COVID-19 pandemic: temporal infoveillance study. J Med Internet Res. 2020;22(10):e22624. doi:10.2196/22624
  • WHO COVID-19 research database; WHO. Available from: https://search.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/. Accessed March 3, 2023.
  • WHO COVID-19 research database: user guide and information; WHO. Available from: https://www.who.int/publications/m/item/quick-search-guide-who-covid-19-database. Accessed March 3, 2023.
  • Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. doi:10.1371/journal.pmed.1000097
  • Armijo-Olivo S, Stiles CR, Hagen NA, Biondo PD, Cummings GG. Assessment of study quality for systematic reviews: a comparison of the Cochrane Collaboration Risk of Bias Tool and the Effective Public Health Practice Project Quality Assessment Tool: methodological research. J Evaluation Clin Practice. 2012;18(1):12–18. doi:10.1111/j.1365-2753.2010.01516.x
  • Impouma B, Mboussou F, Wolfe CM, et al. COVID-19 in the WHO African region: using risk assessment to inform decisions on public health and social measures. Epidemiol Infect. 2021:149. doi:10.1017/s0950268821001126
  • Krueger T, Gogolewski K, Bodych M, et al. Risk assessment of COVID-19 epidemic resurgence in relation to SARS-CoV-2 variants and vaccination passes. Communicat Med. 2022;2(1):84. doi:10.1038/s43856-022-00084-w
  • Zhang S, Wang M, Yang Z, Zhang B. A novel predictor for micro-scale COVID-19 risk modeling: an empirical study from a spatiotemporal perspective. Int J Environ Res Public Health. 2021;18(24):e13294. doi:10.3390/ijerph182413294
  • Liu Y, Zheng F, Du Z, et al. Evaluation of China’s Hubei control strategy for COVID-19 epidemic: an observational study. BMC Infect Dis. 2021;21(1). doi:10.1186/s12879-021-06502-z
  • Nazia N, Law J, Butt ZA. Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling. Sci Rep. 2022;12(1). doi:10.1038/s41598-022-13403-x
  • Wani MA, Farooq J, Wani DM. Risk assessment of COVID-19 pandemic using deep learning model for J&K in India: a district level analysis. Environ. Sci. Pollut. Res. 2021;29(12):18271–18281. doi:10.1007/s11356-021-17046-9
  • Donnat C, Bunbury F, Kreindler J, et al. Predicting COVID-19 transmission to inform the management of mass events: model-based approach. JMIR Public Health Surveillance. 2021;7(12):e30648. doi:10.2196/30648
  • Pluchino A, Biondo AE, Giuffrida N, et al. A novel methodology for epidemic risk assessment of COVID-19 outbreak. Sci Rep. 2021;11(1). doi:10.1038/s41598-021-82310-4
  • Li Q, Tang Z, Coleman N, Mostafavi A. Detecting early-warning signals in time series of visits to points of interest to examine population response to COVID-19 pandemic. IEEE Access. 2021;9:27189–27200. doi:10.1109/access.2021.3058568
  • Shimizu K, Negita M. Lessons learned from Japan’s response to the first wave of COVID-19: a content analysis. Healthcare. 2020;8(4):426. doi:10.3390/healthcare8040426
  • Parajuli RR, Mishra B, Banstola A, et al. Multidisciplinary approach to COVID-19 risk communication: a framework and tool for individual and regional risk assessment. Sci Rep. 2020;10(1). doi:10.1038/s41598-020-78779-0
  • Jia JS, Lu X, Yuan Y, Xu G, Jia J, Christakis NA. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature. 2020;582(7812):389–394. doi:10.1038/s41586-020-2284-y
  • Li T, Luo J, Huang C. Understanding small Chinese cities as COVID-19 hotspots with an urban epidemic hazard index. Sci Rep. 2021;11(1). doi:10.1038/s41598-021-94144-1
  • Grima S, Rupeika-Apoga R, Kizilkaya M, Romānova I, Gonzi RD, Jakovljevic M. A proactive approach to identify the exposure risk to COVID-19: validation of the pandemic risk exposure measurement (PREM) model using real-world data. Risk Management Healthcare Policy. 2021;14:4775–4787. doi:10.2147/rmhp.s341500
  • Amer F, Hammoud S, Farran B, Boncz I, Endrei D. Assessment of countries’ preparedness and lockdown effectiveness in fighting COVID-19. Disaster Medicine and Public Health Preparedness. 2020;15(2):e15–e22. doi:10.1017/dmp.2020.217
  • Yao L, Dong W, Wan JY, Howard SC, Li M, Graff JC. Graphical trajectory comparison to identify errors in data of COVID-19: a cross-country analysis. J Personalized Med. 2021;11(10):955. doi:10.3390/jpm11100955
  • Chakraborty T, Ghosh I. Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: a data-driven analysis. Chaos, Solitons Fractals. 2020;135:e109850. doi:10.1016/j.chaos.2020.109850
  • Maged A, Ahmed A, Haridy S, Baker AW, Xie M. SEIR model to address the impact of face masks amid COVID-19 pandemic. Risk Anal. 2022;43(1):129–143. doi:10.1111/risa.13958
  • Vieira KM, Potrich ACG, Bressan AA, Klein LL, Pereira BAD, Pinto NGM. A pandemic risk perception scale. Risk Anal. 2021;42(1):69–84. doi:10.1111/risa.13802
  • Guo Z, Liu X, Zhao P. A vector field approach to estimating environmental exposure using human activity data. ISPRS Int J Geo-Information. 2022;11(2):135. doi:10.3390/ijgi11020135
  • Kanga S, Meraj G, Sudhanshu M, Nathawat M. Analyzing the risk to COVID-19 infection using remote sensing and GIS. Risk Anal. 2021;41(5):801–813. doi:10.1111/risa.13724
  • Mahmood M, Mateu J, Hernández-Orallo E. Contextual contact tracing based on stochastic compartment modeling and spatial risk assessment. Stochastic Environ Res Risk Assessment. 2021;36(3):893–917. doi:10.1007/s00477-021-02065-2
  • Kheirallah KA, Al-Nusair M, Aljabeiti S, et al. Jordan’s pandemic influenza preparedness (PIP): a reflection on COVID-19 response. Int J Environ Res Public Health. 2022;19(12):7200. doi:10.3390/ijerph19127200
  • Barthe G, Viti RD, Druschel P, et al. Listening to Bluetooth beacons for epidemic risk mitigation. Sci Rep. 2022;12(1). doi:10.1038/s41598-022-09440-1
  • Baik I. Region-specific COVID-19 risk scores and nutritional status of a high-risk population based on individual vulnerability assessment in the national survey data. Clin Nutr. 2022;41(12):3100–3105. doi:10.1016/j.clnu.2021.02.019
  • Zhang Y, Li Y, Yang B, Zheng X, Chen M. Risk assessment of COVID-19 based on multisource data from a geographical viewpoint. IEEE Access. 2020;8:125702–125713. doi:10.1109/access.2020.3004933
  • Grima S, Kizilkaya M, Rupeika-Apoga R, Romānova I, Gonzi RD, Jakovljevic M. A country pandemic risk exposure measurement model. Risk Management Healthcare Policy. 2020;13:2067–2077. doi:10.2147/rmhp.s270553
  • Bird JJ, Barnes CM, Premebida C, Ekárt A, Faria DR. Country-level pandemic risk and preparedness classification based on COVID-19 data: a machine learning approach. PLoS One. 2020;15(10):e0241332. doi:10.1371/journal.pone.0241332
  • Zhou L, Liu J-M, Dong X-P, McGoogan JM, Wu Z-Y. COVID-19 seeding time and doubling time model: an early epidemic risk assessment tool. Infect Diseases Poverty. 2020;9(1). doi:10.1186/s40249-020-00685-4
  • Gunthe SS, Patra SS. Impact of international travel dynamics on domestic spread of 2019-nCoV in India: origin-based risk assessment in importation of infected travelers. Globalization Health. 2020;16(1). doi:10.1186/s12992-020-00575-2
  • Wang Y, Wang L, Zhao X, et al. A semi-quantitative risk assessment and management strategies on COVID-19 infection to outpatient health care workers in the post-pandemic period. Risk Management Healthcare Policy. 2021;14:815–825. doi:10.2147/rmhp.s293198
  • Liu J, Liu L, Tu Y, Li S, Li Z. Multi-stage Internet public opinion risk grading analysis of public health emergencies: an empirical study on microblog in COVID-19. Information Processing and Management. 2022;59(1):e102796. doi:10.1016/j.ipm.2021.102796
  • Ronchi E, Lovreglio R. EXPOSED: an occupant exposure model for confined spaces to retrofit crowd models during a pandemic. Safety Science. 2020;130:e104834. doi:10.1016/j.ssci.2020.104834
  • Wang Z, Yao M, Meng C, Claramunt C. Risk assessment of the overseas imported COVID-19 of ocean-going ships based on AIS and infection data. ISPRS Int J Geo-Information. 2020;9(6):351. doi:10.3390/ijgi9060351
  • Tyagi N, Gurian PL, Kumar A. Using QMRA to understand possible exposure risks of SARS-CoV-2 from the water environment. Environ. Sci. Pollut. Res. 2021;29(5):7240–7253. doi:10.1007/s11356-021-16188-0
  • Gunaratne C, Reyes R, Hemberg E, O’Reilly U-M. Evaluating efficacy of indoor non-pharmaceutical interventions against COVID-19 outbreaks with a coupled spatial-SIR agent-based simulation framework. Sci Rep. 2022;12(1). doi:10.1038/s41598-022-09942-y
  • Trentini F, Manna A, Balbo N, et al. Investigating the relationship between interventions, contact patterns, and SARS-CoV-2 transmissibility. Epidemics. 2022;40:e100601. doi:10.1016/j.epidem.2022.100601
  • Cheng Q, Zheng S, Xiong Z, Lin M. Characterizing the dynamic evolution of interagency collaborative decision-making networks in response to COVID-19 in China: a policy document analysis. Healthcare. 2022;10(3):590. doi:10.3390/healthcare10030590
  • Chu AMY, Chan TWC, So MKP, Wong W-K. Dynamic network analysis of COVID-19 with a latent pandemic space model. Int J Environ Res Public Health. 2021;18(6):3195. doi:10.3390/ijerph18063195
  • Pan J, Tian J, Xiong H, et al. Risk assessment and evaluation of China’s policy to prevent COVID-19 cases imported by plane. PLoS Negl Trop Dis. 2020;14(12):e0008908. doi:10.1371/journal.pntd.0008908
  • Jian S-W, Kao C-T, Chang Y-C, Chen P-F, Liu D-P. Risk assessment for COVID-19 pandemic in Taiwan. Inter J Infect Dis. 2021;104:746–751. doi:10.1016/j.ijid.2021.01.042
  • Kandel N, Chungong S, Omaar A, Xing J. Health security capacities in the context of COVID-19 outbreak: an analysis of International Health Regulations annual report data from 182 countries. Lancet. 2020;395(10229):1047–1053. doi:10.1016/s0140-6736(20)30553-5
  • Duffey RB, Zio E. Prediction of COVID-19 infection, transmission and recovery rates: a new analysis and global societal comparisons. Safety Science. 2020;129:e104854. doi:10.1016/j.ssci.2020.104854
  • Nielsen J, Rod NH, Vestergaard LS, Lange T. Estimates of mortality attributable to COVID-19: a statistical model for monitoring COVID-19 and seasonal influenza, Denmark, spring 2020. Eurosurveillance. 2021;26(8). doi:10.2807/1560-7917.es.2021.26.8.2001646
  • Jung S-m, Akhmetzhanov AR, Hayashi K, et al. Real-time estimation of the risk of death from novel coronavirus (COVID-19) infection: inference using exported cases. J Clin Med. 2020;9(2):523. doi:10.3390/jcm9020523
  • Marani M, Katul GG, Pan WK, Parolari AJ. Intensity and frequency of extreme novel epidemics. Proc Natl Acad Sci. 2021;118(35). doi:10.1073/pnas.2105482118
  • Zhao K, Long C, Wang Y, Zeng T, Fu X. Negligible risk of the COVID-19 resurgence caused by work resuming in China (outside Hubei): a statistical probability study. J Public Health. 2020;42(3):651–652. doi:10.1093/pubmed/fdaa046
  • So MKP, Chu AMY, Tiwari A, Chan JNL. On topological properties of COVID-19: predicting and assessing pandemic risk with network statistics. Sci Rep. 2021;11(1). doi:10.1038/s41598-021-84094-z
  • Ouerfelli N, Vrinceanu N, Coman D, Cioca AL. Empirical modeling of COVID-19 evolution with high/direct impact on public health and risk assessment. Int J Environ Res Public Health. 2022;19(6):3707. doi:10.3390/ijerph19063707
  • Xiang L, Ma S, Yu L, Wang W, Yin Z. Modeling the global dynamic contagion of COVID-19. Front Public Health. 2022;9. doi:10.3389/fpubh.2021.809987
  • Bekesiene S, Samoilenko I, Nikitin A, Meidute-Kavaliauskiene I. The complex systems for conflict interaction modelling to describe a non-trivial epidemiological situation. Mathematics. 2022;10(4):537. doi:10.3390/math10040537
  • Nasker SS, Nanda A, Ramadass B, Nayak S. Epidemiological analysis of SARS-CoV-2 transmission dynamics in the state of Odisha, India: a yearlong exploratory data analysis. Int J Environ Res Public Health. 2021;18(21):e11203. doi:10.3390/ijerph182111203
  • Jen T-H, Chien T-W, Yeh Y-T, Lin J-CJ, Kuo S-C, Chou W. Geographic risk assessment of COVID-19 transmission using recent data. Medicine. 2020;99(24):e20774. doi:10.1097/md.0000000000020774
  • Adler P, Florida R, Hartt M. Mega regions and pandemics. Tijdschrift voor Economische En Sociale Geografie. 2020;111(3):465–481. doi:10.1111/tesg.12449
  • d’Almeida S. Impact of vaccine and immunity passports in the context of COVID-19: a time series analysis in overseas France. Vaccines. 2022;10(6):852. doi:10.3390/vaccines10060852
  • Padilla L, Hosseinpour H, Fygenson R, Howell J, Chunara R, Bertini E. Impact of COVID-19 forecast visualizations on pandemic risk perceptions. Sci Rep. 2022;12(1). doi:10.1038/s41598-022-05353-1
  • So MKP, Tiwari A, Chu AMY, Tsang JTY, Chan JNL. Visualizing COVID-19 pandemic risk through network connectedness. Inter J Infect Dis. 2020;96:558–561. doi:10.1016/j.ijid.2020.05.011
  • Capon A, Sheppeard V, Gonzalez N, et al. Bondi and beyond: lessons from three waves of COVID-19 from 2020. Public Health Res Practice. 2021;31(3). doi:10.17061/phrp3132112
  • Khankeh H, Farrokhi M, Roudini J, et al. Challenges to manage pandemic of coronavirus disease (COVID-19) in Iran with a special situation: a qualitative multi-method study. BMC Public Health. 2021;21(1). doi:10.1186/s12889-021-11973-5
  • Lanyero B, Edea ZA, Musa EO, et al. Readiness and early response to COVID-19: achievements, challenges and lessons learnt in Ethiopia. BMJ Global Health. 2021;6(6):e005581. doi:10.1136/bmjgh-2021-005581
  • Proverbio D, Kemp F, Magni S, Gonçalves J. Performance of early warning signals for disease re-emergence: a case study on COVID-19 data. PLOS Computational Biology. 2022;18(3):e1009958. doi:10.1371/journal.pcbi.1009958
  • Kim I, Lee J, Lee J, Shin E, Chu C, Lee SK. KCDC risk assessments on the initial phase of the COVID-19 outbreak in Korea. Osong Public Health Res Perspectives. 2020;11(2):67–73. doi:10.24171/j.phrp.2020.11.2.02
  • Pettman TL, Hall BJ, Waters E, de Silva-Sanigorski A, Armstrong R, Doyle J. Communicating with decision-makers through evidence reviews. J Public Health. 2011;33(4):630–633. doi:10.1093/pubmed/fdr092
  • Ioannidis JPA. Why most published research findings are false. PLoS Med. 2005;2(8):e124. doi:10.1371/journal.pmed.0020124
  • Chalmers I, Glasziou P. Avoidable waste in the production and reporting of research evidence. Lancet. 2009;374(9683):86–89. doi:10.1016/s0140-6736(09)60329-9