When was the novel Coronavirus first reported?
December 2019
Identification of COVID-19 Can be Quicker through Artificial Intelligence framework using a Mobile Phone-Based Survey in the Populations when Cities/Towns Are Under Quarantine

https://doi.org/10.1017/ice.2020.61

SHA: 83c96f2a481be06a5c58552cbad2ca67ce789dc2

Authors: Vazquez, Arni S.R. Srinivasa Rao; Jose A.
Date: 2020
DOI: 10.1017/ice.2020.61
License: cc-by

Abstract: We are proposing to use machine learning algorithms to be able to improve possible case identifications of COVID-19 more quicker when we use a mobile phone-based web survey. This will also reduce the spread in the susceptible populations. Text: Emerging and novel pathogens are a significant problem for global public health and technology can assist in faster identification of possible cases to bring timely interventions. This is especially true for viral diseases that are easily and readily transmissible and have asymptomatic infectivity periods. The novel Coronavirus (SARSCoV2) described in December 2019 has resulted in major quarantines throughout the world, including major cites, villages and public areas throughout China [1] [2] [3] to prevent further spread. As of February 25 th 2020, the World Health Organization's situational data indicates that there were about 77780 confirmed cases, including 2666 deaths due to COVID-19, including cases in 25 countries [4] . The majority of the deaths reported so far have been in China only [5] .

Organization have issued interim guidelines in order to protect the population, and to attempt to prevent the further spread of COVID-19 from infected individuals [6] . Since cities and villages throughout China are unable to accommodate such large numbers of infected individuals, and be able to maintain the quarantined. China has built several new hospitals in an attempt to manage the infected individuals [7] . It is imperative that we evaluate novel models in an attempt to control the rapidly spreading virus [8] . In order to reduce the time to identification of a person under investigation (PUI) for the COVID-19 infection, and the rapid isolation of this individual, we propose to collect the basic travel history along with the more common manifestations using a phone-based online survey. Such collected data can be used to assist in the preliminary screening and early identification of possible COVID-19 infected individuals. Thousands of data points are able to be collected and processed through an artificial intelligence (AI) framework which can ultimately evaluate individuals that may be infected and stratify them into no-risk, minimal-risk, moderate-risk, and high-risk of being infected with the virus. The identification of the high-risk cases can then be quarantined earlier, thus decreasing the chance of spread. Table 1 is inserted here. See Appendix I for the details on the steps involved in data collection on all the respondents independent of whether or not they think they are infected. The AI algorithm described in Appendix II is to identify possible case identifications and send alerts to the nearest health clinic as well as to the respondent for an immediate health visit, we call this as an "alert for health check recommendation for COVID-2019. In case the respondent is unable to commute to the health center, the health department can then send an alert to a mobile health unit so they can then do doorto-door assessments and even testing for the virus. This generates alert for mobile health check recommendation for 2019-nCoV (MHCRC). If a respondent does not have an immediate risk of having symptoms or signs related to the viral infection, then the AI-based health alert will be sent to the respondent to notify them that there is no current risk of COVID-2019. Figure 1 summarizes the outcomes of data collection and identification of possible cases. The data recorded in step 5 of the algorithm using signs and symptoms will be collected prior to both the groups who have received alerts HCRC or MHCRC (for possible identification and assessment) and NCRC (for non-identified respondents). These are explained in steps (iii) and (iv) in the Appendix II. The extended analysis proposed will help to understand if there is any association with different sociodemographic variables and the manifestations such as fever and signs and lower respiratory infections, including cough and SOB in individuals defined as either with and without possible infection. Applications of AI and deep learning argued to be useful tools in assisting diagnosis and treatment decision making [10] [11] . There were studies which promoted disease detection through AI models [12] [13] [14] [15] . Use of mobile phones [16] [17] [18] [19] and web based portals [20] [21] have been tested successfully in health related data collection. However, one need to apply such techniques in a timely way for faster results. Apart from cost-effectiveness, the proposed modeling will be of great assistance in identifying and controlling when populations are closed due to virus spread. In addition to these, our proposed algorithm can be easily extended to identify individuals who might have any mild symptoms and signs.

We have developed our data collection criteria based on CDC's Flowchart to Identify and Assess 2019 Novel Coronavirus [9] and added additional variables for the extended utility of our efforts in identifying infected and controlling the spread (see Table 1 ). Let be the outputs recorded during the data collection steps 1 (ii) If the set of identifiers, , for is equal to one of the elements of the set then send HCRC or MHCRC to that respondent, else proceed to the test criteria (iv).

If is equal to one of the elements of the set , for then the respondent will be sent an NCRC alert.

(iv)

If is equal to one of the elements of the set , then the respondent will be sent an NCRC alert.

Comparison of test criteria results of (iii) and (iv) with their corresponding geographic and socio-demographic details will yield further investigations of signs and symptoms Suppose we define two events and using the sets and as below:

: out of responded cases are identified through the algorithm out of have responded to the survey.

The conditional probability of the event given the event , say, is computed as