Artificial Intelligence Against the First Wave of COVID-19: Evidence from China

Overview

Journal BMC Health Serv Res

Publisher Biomed Central

Specialty Health Services

Date 2022 Jun 10

PMID 35689275

Authors

Ting Wang

Yi Zhang

Chun Liu

Zhongliang Zhou

Affiliations

Soon will be listed here.

Abstract

Background: The COVID-19 pandemic unexpectedly broke out at the end of 2019. Due to the highly contagious, widespread, and risky nature of this disease, the pandemic prevention and control has been a tremendous challenge worldwide. One potentially powerful tool against the COVID-19 pandemic is artificial intelligence (AI). This study systematically assessed the effectiveness of AI in infection prevention and control during the first wave of COVID-19 in China. METHODS: To better evaluate the role of AI in a pandemic emergency, we focused on the first-wave COVID-19 in the period from the early December 2019 to the end of April 2020 across 304 cities in China. We employed three sets of dependent variables to capture various dimensions of the effect of AI: (1) the time to the peak of cumulative confirmed cases, (2) the case fatality rate and whether there were severe cases, and (3) the number of local policies for work and production resumption and the time span to having the first such policy. The main explanatory variable was the local AI development measured by the number of AI patents. To fit the features of different dependent variables, we employed a variety of estimation methods, including the OLS, Tobit, Probit, and Poisson estimations. We included a large set of control variables and added interaction terms to test the mechanisms through which AI took an effect.

Results: Our results showed that AI had highly significant effects on (1) screening and detecting the disease, and (2) monitoring and evaluating the epidemic evolution. Specifically, AI was useful to screen and detect the COVID-19 in cities with high cross-city mobility. Also, AI played an important role for production resumption in cities with high risk to reopen. However, there was limited evidence supporting the effectiveness of AI in the diagnosis and treatment of the disease.

Conclusions: These results suggested that AI can play an important role against the pandemic.

Citing Articles

High connectivity and human movement limits the impact of travel time on infectious disease transmission.

John R, Miller J, Muylaert R, Hayman D J R Soc Interface. 2024; 21(210):20230425.

PMID: 38196378 PMC: 10777149. DOI: 10.1098/rsif.2023.0425.

Bibliometric analysis of the use of artificial intelligence in COVID-19 based on scientific studies.

Karbasi Z, Gohari S, Sabahi A Health Sci Rep. 2023; 6(5):e1244.

PMID: 37152228 PMC: 10158785. DOI: 10.1002/hsr2.1244.

References

Jia P, Dong W, Yang S, Zhan Z, Tu L, Lai S . Spatial Lifecourse Epidemiology and Infectious Disease Research. Trends Parasitol. 2020; 36(3):235-238. PMC: 7172117. DOI: 10.1016/j.pt.2019.12.012. View

Leung K, Wu J, Liu D, Leung G . First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: a modelling impact assessment. Lancet. 2020; 395(10233):1382-1393. PMC: 7195331. DOI: 10.1016/S0140-6736(20)30746-7. View

Wu J, Wang J, Nicholas S, Maitland E, Fan Q . Application of Big Data Technology for COVID-19 Prevention and Control in China: Lessons and Recommendations. J Med Internet Res. 2020; 22(10):e21980. PMC: 7561444. DOI: 10.2196/21980. View

Niu Y, Xu F . Deciphering the power of isolation in controlling COVID-19 outbreaks. Lancet Glob Health. 2020; 8(4):e452-e453. PMC: 7269718. DOI: 10.1016/S2214-109X(20)30085-1. View

Feigin V, Lawes C, Bennett D, Anderson C . Stroke epidemiology: a review of population-based studies of incidence, prevalence, and case-fatality in the late 20th century. Lancet Neurol. 2003; 2(1):43-53. DOI: 10.1016/s1474-4422(03)00266-7. View

Chen S, Yang J, Yang W, Wang C, Barnighausen T . COVID-19 control in China during mass population movements at New Year. Lancet. 2020; 395(10226):764-766. PMC: 7159085. DOI: 10.1016/S0140-6736(20)30421-9. View

Franch-Pardo I, Napoletano B, Rosete-Verges F, Billa L . Spatial analysis and GIS in the study of COVID-19. A review. Sci Total Environ. 2020; 739:140033. PMC: 7832930. DOI: 10.1016/j.scitotenv.2020.140033. View

Bian L, Gao Q, Gao F, Wang Q, He Q, Wu X . Impact of the Delta variant on vaccine efficacy and response strategies. Expert Rev Vaccines. 2021; 20(10):1201-1209. PMC: 8442750. DOI: 10.1080/14760584.2021.1976153. View

Topol E . High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019; 25(1):44-56. DOI: 10.1038/s41591-018-0300-7. View

10.

Troiano G, Nardi A . Vaccine hesitancy in the era of COVID-19. Public Health. 2021; 194:245-251. PMC: 7931735. DOI: 10.1016/j.puhe.2021.02.025. View

11.

Prem K, Liu Y, Russell T, Kucharski A, Eggo R, Davies N . The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. Lancet Public Health. 2020; 5(5):e261-e270. PMC: 7158905. DOI: 10.1016/S2468-2667(20)30073-6. View

12.

Wu Z, McGoogan J . Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. JAMA. 2020; 323(13):1239-1242. DOI: 10.1001/jama.2020.2648. View

13.

Pedone D, Moglianetti M, De Luca E, Bardi G, Pompa P . Platinum nanoparticles in nanobiomedicine. Chem Soc Rev. 2017; 46(16):4951-4975. DOI: 10.1039/c7cs00152e. View

14.

Tian H, Liu Y, Li Y, Wu C, Chen B, Kraemer M . An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science. 2020; 368(6491):638-642. PMC: 7164389. DOI: 10.1126/science.abb6105. View

15.

Altmann D, Douek D, Boyton R . What policy makers need to know about COVID-19 protective immunity. Lancet. 2020; 395(10236):1527-1529. PMC: 7185915. DOI: 10.1016/S0140-6736(20)30985-5. View

16.

Yang Z, Zeng Z, Wang K, Wong S, Liang W, Zanin M . Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. J Thorac Dis. 2020; 12(3):165-174. PMC: 7139011. DOI: 10.21037/jtd.2020.02.64. View

17.

Srinivasa Rao A, Vazquez J . Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey when cities and towns are under quarantine. Infect Control Hosp Epidemiol. 2020; 41(7):826-830. PMC: 7200852. DOI: 10.1017/ice.2020.61. View

18.

Lau H, Khosrawipour T, Kocbach P, Ichii H, Bania J, Khosrawipour V . Evaluating the massive underreporting and undertesting of COVID-19 cases in multiple global epicenters. Pulmonology. 2020; 27(2):110-115. PMC: 7275155. DOI: 10.1016/j.pulmoe.2020.05.015. View

19.

Hellewell J, Abbott S, Gimma A, Bosse N, Jarvis C, Russell T . Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Glob Health. 2020; 8(4):e488-e496. PMC: 7097845. DOI: 10.1016/S2214-109X(20)30074-7. View

20.

Ji Y, Ma Z, Peppelenbosch M, Pan Q . Potential association between COVID-19 mortality and health-care resource availability. Lancet Glob Health. 2020; 8(4):e480. PMC: 7128131. DOI: 10.1016/S2214-109X(20)30068-1. View