Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve

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Abstract

The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9–30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city.

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Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020

Kenji Mizumoto, Katsushi Kagaya, Alexander Zarebski … (2020)

On 5 February 2020, in Yokohama, Japan, a cruise ship hosting 3,711 people underwent a 2-week quarantine after a former passenger was found with COVID-19 post-disembarking. As at 20 February, 634 persons on board tested positive for the causative virus. We conducted statistical modelling to derive the delay-adjusted asymptomatic proportion of infections, along with the infections’ timeline. The estimated asymptomatic proportion was 17.9% (95% credible interval (CrI): 15.5–20.2%). Most infections occurred before the quarantine start.

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Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case

Dmitry Ivanov (2020)

Highlights • Epidemic outbreaks are a special case of supply chain (SC) risks. • We articulate the specific features of epidemic outbreaks in SCs. • We demonstrate a simulation model for epidemic outbreak analysis. • We use an example of coronavirus COVID-19 outbreak.

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Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19)

Hiroshi Nishiura, Tetsuro Kobayashi, Takeshi Miyama … (2020)

The number of novel coronavirus (COVID-19) cases worldwide continues to grow, and the gap between reports from China and statistical estimates of incidence based on cases diagnosed outside China indicates that a substantial number of cases are underdiagnosed (Nishiura et al., 2020a). Estimation of the asymptomatic ratio—the percentage of carriers with no symptoms—will improve understanding of COVID-19 transmission and the spectrum of disease it causes, providing insight into epidemic spread. Although the asymptomatic ratio is conventionally estimated using seroepidemiological data (Carrat et al., 2008, Hsieh et al., 2014), the collection of these data requires significant logistical effort, time, and cost. Instead, we propose a method of estimating the asymptomatic ratio by using information on Japanese nationals who were evacuated from Wuhan, China on charter flights. Figure 1 illustrates the flow of the evacuation process. By February 6, 2020 a total of N = 565 citizens had been evacuated. Among them, pN = 63 (11.2%) were considered symptomatic upon arrival based on (1) temperature screening before disembarkation, and (2) face-to-face interviews eliciting information on symptoms including fever, cough, and other non-specific symptoms consistent with COVID-19. Reverse transcription PCR (RT-PCR) testing was performed for all passengers, and m = 4 asymptomatic and n = 9 symptomatic passengers tested positive for COVID-19. Figure 1 Flow diagram of symptom screening and viral testing for passengers on chartered evacuation flights from Wuhan, China to Japan. The flow of Japanese residents evacuating from Wuhan and screened in Japan. A total of N passengers were evaluated, of whom a fraction p were symptomatic upon arrival. Among symptomatic and asymptomatic individuals, n and m persons, respectively, tested positive for the virus by RT-PCR. Figure 1 Employing a Bayes theorem, the asymptomatic ratio is defined as P r ( a s y m p t o m a t i c | i n f e c t i o n ) = Pr i n f e c t i o n | a s y m p t o m a t i c P r ( a s y m p t o m a t i c ) P r ( i n f e c t i o n ) , which can be calculated as m/(n + m), as seen in Figure 1. Using a binomial distribution, the asymptomatic ratio among evacuees was thus estimated to be 30.8% (95% confidence interval 7.7–53.8%). On March 6, 2020, a minimum of 30 days had elapsed since the citizens had departed from Wuhan – a length of observation sufficiently longer than the COVID-19 incubation period (Li et al., 2020, Linton et al., 2020). Thus, there was very little probability that the four virus-positive asymptomatic individuals would develop symptoms. In general, asymptomatic infections cannot be recognized if they are not confirmed by RT-PCR or other laboratory testing, and symptomatic cases may not be detected if they do not seek medical attention (Nishiura et al., 2020b). Estimates such as this therefore provide important insight by using a targeted population to assess the prevalence of asymptomatic viral shedding (Kupferschmidt and Cohen, 2020). It should be noted that the limited sensitivity of RT-PCR does not affect the estimate of the asymptomatic ratio, because the sensitivity is cancelled out from the right-hand side of the equation. However, a weakness of this study is that age-dependence and other aspects of heterogeneity were ignored, because the samples relied on Japanese evacuees from Wuhan. Despite the small sample size, this estimation indicates that perhaps less than a half of COVID-19-infected individuals are asymptomatic. This ratio is slightly smaller than that for influenza, which has been estimated at 56–80% (Hsieh et al., 2014) using similar definitions for symptomatic individuals. There is great need for further studies on the prevalence of asymptomatic COVID-19 infections to guide epidemic control efforts. Ethical approval Not required. Conflict of interest We declare that we have no conflict of interest.

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Author and article information

Journal

Journal ID (nlm-ta): Int J Environ Res Public Health

Journal ID (iso-abbrev): Int J Environ Res Public Health

Journal ID (publisher-id): ijerph

Title: International Journal of Environmental Research and Public Health

Publisher: MDPI

ISSN (Print): 1661-7827

ISSN (Electronic): 1660-4601

Publication date (Electronic): 14 May 2020

Publication date (Print): May 2020

Volume: 17

Issue: 10

Electronic Location Identifier: 3437

Affiliations

School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada; murat.simsek@ 123456uottawa.ca

Author notes

[* ]Correspondence: burak.kantarci@ 123456uottawa.ca ; Tel.: +1-613-562-5800 (ext. 6955)

[†]

All authors contributed equally to this work.

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Burak Kantarci https://orcid.org/0000-0003-0220-7956

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Publisher ID: ijerph-17-03437

DOI: 10.3390/ijerph17103437

PMC ID: 7277766

PubMed ID: 32423150

SO-VID: 55ab1cf4-1fc6-4bb1-b09d-fe138ab94790

License:

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve

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Most cited references 25

Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020

Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case

Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19)

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