Use of Artificial Intelligence on spatio-temporal data to generate insights during COVID-19 pandemic: A Review

TL;DR: This review presents a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19 and lists potential paths of research for which AI based techniques can be used for greater impact in tackling the pandemic.

Abstract: The COVID-19 pandemic, within a short time span, has had a significant impact on every aspect of life in almost every country on the planet. As it evolved from a local epidemic isolated to certain regions of China, to the deadliest pandemic since the influenza outbreak of 1918, scientists all over the world have only amplified their efforts to combat it. In that battle, Artificial Intelligence, or AI, with its wide ranging capabilities and versatility, has played a vital role and thus has had a sizable impact. In this review, we present a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19. Furthermore, we catalogue the articles in these areas based on spatio-temporal modeling, intrinsic parameters, extrinsic parameters, dynamic parameters and multivariate inputs (to ascertain the penetration of AI usage in each sub area). The manner in which AI is used and the associated techniques utilized vary for each body of work. Majority of articles use deep learning models, compartment models, stochastic methods and numerous statistical methods. We conclude by listing potential paths of research for which AI based techniques can be used for greater impact in tackling the pandemic.

Summary

1. Introduction

• The COVID-19 pandemic caused by the novel coronavirus (SARS-CoV-2) exemplifies the vulnerabilities of healthcare systems in the face of unforeseen adversity.
• In fact, AI systems assist healthcare by performing tasks such as diagnosis, risk assessment, forecasting and surveillance, and health policy and planning.
• To assist further research in the field, this plethora of articles have been summarized and evaluated in several review articles.
• These review articles discuss the impact of AI in a general sense i.e. without further categorization into specific topics.

2. Search Strategy and Selection Criteria

• The literature survey for research papers on existing work on COVID-19 (or pandemics in general) and AI was done using IEEE Xplore, PubMed and Google Scholar for articles published between the time period 2019 and 2020 September.
• Only articles written in English were considered in this review.
• The keywords used were [”COVID-19”, ”pandemic” or ”sars-cov-2”] and [”AI” or ”artificial intelligence” or ”machine learning” or ”pattern recognition” or ”prediction” or ”forecasting”] and [”time” or ”location”, ”demography” or ”country” or ”spatio-temporal”].
• Papers were shortlisted from peer reviewed papers published or in press for journals indexed in Scimago.
• The texts were examined to pick 49 research papers that matched the scope of this survey.

3. Results

• The authors literature survey showed that spatio-temporal forecasting was performed either as a time series forecasting problem or as a diversity aware analysis by taking the diversity of populations into account via one/multiple parameter(s).
• The parameters being used are split into groups based on whether the parameter is inherent to the individuals , the environment or an activity performed by the individuals .
• The papers are grouped into sections depending on the parameters used to model the affected parties as given in Table 1.

3.1. Spatio-Temporal modeling and forecasting

• The first cluster of research papers on modeling and forecasting was based on spatial and temporal data alone.
• 15 Several methods including statistical approaches, machine learning techniques, deep learning models, and time series models have been evaluated in this landscape.
• The various AI models used and example papers are listed in Fig. 3.

Temporal modeling:

• Temporal forecasting of COVID-19 infection cases have been evaluated using multiple deep learning models.
• The Bi-LSTM model and the ConvLSTM models are shown to outperform the other LSTM variants with a Mean Absolute Percentage Error (MAPE) of 3% for day ahead predictions.
• A novel statistical method has been developed to forecast the spread of COVID-19 globally.
• This method considers lead-lag effects between different time series using the dynamic time warping technique to analyze non linear relationships among nations.
• The study concludes that the best parameter to assess the effectiveness of confinement and risk of virus diffusion, is the average number of daily contacts in a population (c).

Spatio-temporal modeling:

• Researchers have focused on modeling and forecasting the spread of COVID-19 in the spatio-temporal domain to identify not only ‘‘how much” the disease has spread but also ‘‘where” it has spread to.
• The model takes into account effects of different containment strategies, event and contact restrictions, and different courses the infection may take; which is not possible when dealing with traditional SIR models.
• A novel approach is taken using Moran’s I test to find regional correlations of COVID-19 cases in China.
• The spatial association between states are modelled into the following six types:30 1) Sharing of borders 2) Euclidean distance 3) Population 4) Population density 5) Number of doctors and hospitals 6) Number of medical beds.
• This shows that similar to attributes such as population, underlying spatio-temporal attributes such as inter-state travel can be employed to predict the dynamic spread of the pandemic.

3.2. Modeling the impact on diverse populations

• The authors review methods that group the population into clusters based on different factors such as occupation, age and other such parameters and analyze COVID-19 spread in those demographics.
• An overview of what characterizes the population diversity is given in Fig.
• The importance of this diversity/context awareness of information for handling the pandemic across the globe has been studied.
• Key challenges in this domain are handling non-uniformly sampled data, incomplete data and figuring out the context without it being readily fed into the models.
• A summary of this section is given in Table 2.

Medical Conditions

• A large-scale analysis of the severity of COVID-19 among the population, given the existing statistics of the medical conditions as per the Global Burden of Disease and Population census among the world, is done using statistical analysis coupled with resampling techniques.
• This study shows the importance of utilizing larger data sources to generate insights during the pandemic.
• Another study aiming to uncover a similar correlation between pre-existing conditions and COVID-19 mortality was conducted on individual patient data from Wuhan using linear regression and statistical techniques.

Age

• The authors discuss the research work attempting to uncover the differential impact of COVID-19 on different age groups.
• The age dependence of COVID-19 ’s impact on children is studied using statistical analysis to thereby generate predictions on the population as a whole by considering data from USA.
• This work goes on to generate recommendations based on the predictions on how best to prepare for large scale outbreaks.
• The age stratified CFR rate (case fatality rate -- the ratio of deaths to the number of total cases) is analyzed using basic statistics.
• Another study performs an in-depth analysis on a larger dataset from around the world (China’s statistics coupled with the rest of the world).

Environmental differences

• The spread of COVID-19 has been analyzed with regard to environmental factors such as temperature and humidity.
• One study uses a spatial Seemingly Unrelated Regressions (SUR) to model the COVID-19 spread as an inter-regional contagion process.
• This doesn’t show the effect of temperature and sunshine on the behavior of the virus, but instead analyzes the people’s behavioral changes based on environmental factors, and thereby the spread of the virus as a result of these changes.
• 41 Statistical models are used to show the absence of a correlation between the daily temperature and case counts in Spain.
• 42 Similarly, the performance of ANN based COVID-19 prediction is evaluated to show how absence of weather data does not make a difference.

Social, Economic and Political factors

• This section discusses the effect of social, economic and political dependencies on the spread and severity of COVID-19.
• The study recommends healthcare resources to be directed towards areas where low testing and high positive rates are prevalent.
• 44 Another study explores spatial relationships between socio-demographics and the spread of COVID-19 in three regions of Texas using ordinary least squares (OLS) and geographically weighted regression (GWR).
• This was evidence of how government policies can affect the spread of COVID-19.
• The authors analyze the effects of no intervention, cyclic mitigation or suppression measures followed by relaxation period.

Travelling and Population flow

• In this section the authors focus on travelling and mass population flow between cities during the COVID-19 pandemic prior to the lockdown measures and its impact.
• Using this, a risk analysis was developed using a Bayesian space-time model to quantify the risk of a person migrating from Wuhan spreading COVID-19 to another population.
• 52 While the similar scenario in India’s mass migration has been studied,53 AI is yet to be used in the Indian situation.
• The former was done by a neural network that can absorb time series information of the patient counts and travel statistics, as well as knowledge about the geographical hierarchy of countries and continents.
• The results show that travel restrictions had a minimal effect unless they were supplemented with other behavioral changes to mitigate the disease spread.

3.2.4. Multivariate inputs

• The methods in these articles attempt to model the complex relationships between different parameters and uncover correlations that might not be obvious in traditional statistical analysis.
• The reduction of variables vastly helps real-time forecasting due to simplicity and reduced computational cost.
• 58 Using the information of COVID-19 spread in the city of Lombardy, an individual level model for COVID-19 transmission that depends on geographical location, age, household structure, and current medical conditions was derived.
• The work also shows the importance of understanding the inter-dependence of the input variables to develop deep learning models coupled with human intuition.

4. Discussion and potentials of AI for future avenues.

• Even though medical systems have been computerized in the past decades, the unprecedented nature of COVID-19 has brought forward issues that need to be automated but are still dependent on human intervention.
• The application of AI for such novel issues poses certain challenges, but overcoming these challenges and exploiting the available methodologies could result in a marked improvement in a variety of fields, especially within the medical domain.
• Similarly, traditional techniques do not handle Big Data efficiently.
• It would be interesting to consider these social clustering parameters for this study, as they may have significant correlation with the spread of pandemic.

5. Limitations and Conclusion

• The rapid development and growing research in AI is amassed as preprints rather than peer reviewed work.
• Since a major portion of the AI research and development ends up in commercial deployment, they are not made available to the public.
• Furthermore, India recorded a large number of COVID-19 patients as well and it’s demographics and socio-economics presented a wide range of unique challenges (mass immigration, social inequalities in access to healthcare) in handling the situation.
• Analysis and reports on this matter that were done in local languages are also not considered in this review.

Figures

Figure 4: Impact Modeling. The reference numbers of research papers are given in square brackets.

Figure 3: AI models used in spatio-temporal modeling and forecasting. The reference number for each model is given in square brackets. Green- temporal model, Pink- spatio-temporal model.

Figure 2: Time variation of number of papers in AI (for medical applications only), COVID and AI+COVID

Table 2: Summary : Diversity aware AI

Figure 1: Flowchart for the literature survey methodology

Table 3: Potential of AI

Table 1: Parameters being used to characterize the diversity in population in the literature

Full text

Use of Artificial Intelligence on spatio-temporal data to generate insights
during COVID-19 pandemic: A Review
Gihan Jayatilaka
a,1,∗
, Jameel Hassan
a,1
, Umar Marikkar
a
, Rumali Perera
b
, Suren Sritharan
c
, Harshana
Weligampola
c
, Mevan Ekanayake
d
, Roshan Godaliyadda
a
, Parakrama Ekanayake
a
, Vijitha Herath
a
, G M
Dilshan Godaliyadda
e
, Anuruddhika Rathnayake
f
, Samath D. Dharmaratne
g,h
, Janaka Ekanayake
a,i
a
Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Peradeniya, Peradeniya [20400],
Sri Lanka
b
Department of Statistics and Computer Science, Faculty of Science, University of Peradeniya, Peradeniya [20400], Sri Lanka
c
Faculty of IT and Computing, Sri Lanka Technological Campus, Padukka [10500], Sri Lanka.
d
Office of Research and Innovation Services, Sri Lanka Technological Campus, Padukka [10500], Sri Lanka
e
Researcher, Texas, United States of America
f
Postgraduate Institute of Medicine, University of Colombo, Colombo 07 [07000], Sri Lanka
g
Department of Community Medicine, Faculty of Medicine, University of Peradeniya, Peradeniya [20400], Sri Lanka
h
Department of Heath Metrics Sciences, Institute for Health Metrics and Evaluation, School of Medicine, University of
Washington, United States of America
i
Cardiff School of Engineering, Cardiff University, The Parade, Cardiff CF24 3AA, United Kingdom
Keywords: COVID-19, Artificial Intelligence (AI), Prevention, Control
Abstract
The COVID-19 pandemic, within a short time span, has had a significant impact on every aspect of life
in almost every country on the planet. As it evolved from a local epidemic isolated to certain regions of
China, to the deadliest pandemic since the influenza outbreak of 1918, scientists all over the world have
only amplified their efforts to combat it. In that battle, Artificial Intelligence, or AI, with its wide ranging
capabilities and versatility, has played a vital role and thus has had a sizable impact. In this review, we
present a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting
and impact modeling on diverse populations as it relates to COVID-19. Furthermore, we catalogue the
articles in these areas based on spatio-temporal modeling, intrinsic parameters, extrinsic parameters, dynamic
parameters and multivariate inputs (to ascertain the penetration of AI usage in each sub area). The manner
in which AI is used and the associated techniques utilized vary for each body of work. Majority of articles
∗
Corresponding author. Email: gihanjayatilaka@eng.pdn.ac.lk
1
Equally contributing authors
2020-11-27
. CC-BY-NC 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint
The copyright holder for thisthis version posted November 27, 2020. ; https://doi.org/10.1101/2020.11.22.20232959doi: medRxiv preprint
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

use deep learning models, compartment models, stochastic methods and numerous statistical methods. We
conclude by listing potential paths of research for which AI based techniques can be used for greater impact
in tackling the pandemic.
1. Introduction
The COVID-19 pandemic caused by the novel coronavirus (SARS-CoV-2) exemplifies the vulnerabilities of
healthcare systems in the face of unforeseen adversity. After COVID-19 emerged from China in January 2020,
it has spread across the globe and has been responsible for the ongoing pandemic that has claimed hundreds
of thousands of lives around the world (as of October 18, 2020, over 1
·
1 million deaths have been reported
1
).
Artificial Intelligence (AI) is at the forefront in the global response to the prevailing COVID-19 pandemic. In
particular, AI solutions have been developed for infection detection, forecasting, response planning, recovery
planning, risk assessment and patient prioritization, screening and diagnosis, contact tracing, understanding
social interventions, and automated patient care.
2, 3, 4
AI is the notion of mimicking human intellect on a computer, through a collection of operations. The concept
of AI has been with us since the early 1900s and was formalized by Alan Turing in the 1950s. However,
up until the 21st century AI was at a nascent state primarily due to the limited availability of processing
power. The AI revolution as we know came about in the early 2010s when AlexNet,
5
an artificial deep neural
network-based solution, managed to triumph the competition in an image classification challenge. Since then,
AI has permeated almost every scientific discipline including agriculture, education, manufacturing, finance,
transportation, media, and healthcare. Global Health is another key sector which is widely influenced by AI.
In fact, AI systems assist healthcare by performing tasks such as diagnosis, risk assessment, forecasting and
surveillance, and health policy and planning.
6, 7
The furtherance of AI systems has been supplemented by the abundance of data, i.e. Big Data, that is
available to researchers in the era of COVID-19. Hence, hundreds of articles on AI-based systems for different
tasks in the fight against COVID-19 are published constantly (See Fig. 2). To assist further research in the
field, this plethora of articles have been summarized and evaluated in several review articles.
2, 6, 8, 9, 10, 11, 12
These review articles discuss the impact of AI in a general sense i.e. without further categorization into
specific topics. However, with further proliferation of articles, a need for more rigorous review has emerged,
specifically, reviews that catalogue projects based on their specific utilization of AI i.e. cataloguing based
on which tasks are achieved by AI and how. In this paper, we identify and categorize papers into two
primary categories based on the tasks achieved through AI -- spatio-temporal modeling and forecasting
where features like geographical location and time are utilized for modeling and forecasting, and modeling
the impact on diverse populations where additional features like age, medical conditions, environmental
differences, socio-economic dependence, political dependence, population flow, and travel patterns are utilized
2
. CC-BY-NC 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint
The copyright holder for thisthis version posted November 27, 2020. ; https://doi.org/10.1101/2020.11.22.20232959doi: medRxiv preprint

for modeling. In this review, we avert the normative style of review articles on AI systems for COVID-19
and present an in-depth analysis focusing on the aforementioned two tasks. We also discuss the potential of
AI systems and how the worldwide AI community should respond to COVID-19 in the near term as well as
how we can learn from our experiences for future pandemics.
2. Search Strategy and Selection Criteria
The literature survey for research papers on existing work on COVID-19 (or pandemics in general) and AI
was done using IEEE Xplore, PubMed and Google Scholar for articles published between the time period
2019 and 2020 September. Only articles written in English were considered in this review. The keywords
used were [”COVID-19”, ”pandemic” or ”sars-cov-2”] and [”AI” or ”artificial intelligence” or ”machine
learning” or ”pattern recognition” or ”prediction” or ”forecasting”] and [”time” or ”location”, ”demography”
or ”country” or ”spatio-temporal”]. Papers were shortlisted from peer reviewed papers published or in press
for journals indexed in Scimago. These criteria resulted in 120 research papers. The texts were examined to
pick 49 research papers that matched the scope of this survey. This is illustrated in Fig. 1
3. Results
Our literature survey showed that spatio-temporal forecasting was performed either as a time series forecasting
problem or as a diversity aware analysis by taking the diversity of populations into account via one/multiple
parameter(s). The parameters being used are split into groups based on whether the parameter is inherent to
the individuals (intrinsic), the environment (extrinsic) or an activity performed by the individuals (dynamic).
The papers are grouped into sections depending on the parameters used to model the affected parties as given
in Table 1.
3.1. Spatio-Temporal modeling and forecasting
The first cluster of research papers on modeling and forecasting was based on spatial and temporal data alone.
This enabled us to identify the impact of AI usage on spatio-temporal data to facilitate the fight against
COVID-19 by obtaining predictions on the number of total patients in the future, thus giving insight on the
trajectory a country/state is heading in. It facilitates the future allocation of hospital resources, the lack
of which has caused widespread commotion in many countries.
13, 14
This data is also crucial in instructing
the authorities involved in policy and decision making, and will thereby ensure efficient containment of the
virus spread.
15
Several methods including statistical approaches, machine learning techniques, deep learning
models, and time series models have been evaluated in this landscape. The various AI models used and
example papers are listed in Fig. 3.
3
. CC-BY-NC 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint
The copyright holder for thisthis version posted November 27, 2020. ; https://doi.org/10.1101/2020.11.22.20232959doi: medRxiv preprint

Temporal modeling:
Temporal forecasting of COVID-19 infection cases have been evaluated using multiple deep learning models.
A multi-step prediction using the neural network models - Long Short Term Memory (LSTM), Convolutional
LSTM (ConvLSTM), Bidirectional LSTM (Bi-LSTM)- as a case study for Canada,
16
different states in
India,
17
and USA
18
have been studied. The Bi-LSTM model and the ConvLSTM models are shown to
outperform the other LSTM variants with a Mean Absolute Percentage Error (MAPE) of 3% for day ahead
predictions. Accuracy of variants of LSTMs are further compared with Gated Recurrent Units (GRU),
a Variational Auto-Encoder (VAE), and Support Vector Regression (SVR) models.
19, 20
The models are
evaluated on multiple countries where substantial data is available (USA, Italy, Spain, China, Australia) to
validate that the Bi-LSTM and VAE (MAPE less than 3%) models outperform the conventional models. A
data driven forecasting method has been developed to estimate the number of positive cases of COVID-19 in
India for the next 30 days.
21
The number of recovered cases, daily positive cases, and deceased have also
been estimated using LSTMs and curve fitting. In addition, a machine learning based random forest model
has been used to forecast the number of COVID-19 cases with a mean correlation coefficient R
2
of 0·914.
22
A novel statistical method has been developed to forecast the spread of COVID-19 globally. This method
considers lead-lag effects between different time series using the dynamic time warping technique to analyze
non linear relationships among nations.
23
Thus, it was able to determine underlying causal relationships such
as the origin of the virus and the forerunner in given regions. A Non linear Autoregressive Artificial Neural
Networks (NAR-ANN) and Auto Regressive Integrated Moving Average (ARIMA) model was used to forecast
the spread of COVID-19 in Egypt.
24
An analysis of COVID-19 deaths using reduced space Gaussian process
regression has shown a correlation coefficient of 98
·
9%.
25
In an attempt to solve the patient triage problem
related to COVID-19, researchers in Italy have formulated a discrete time markov chain model to predict
the number of COVID-19 cases, to thereby, efficiently allocate ICU resources across the country.
14
The
critical need for COVID-19 forecasting comes from the need to understand how to implement containment
strategies while balancing its impact on the country’s economy and thereby the livelihood of people. From an
economical impact standpoint, a modified ARIMA model was developed to forecast the number of COVID-19
cases and the stock market in Spain
26
for the given period. ARIMA has also been ensembled with Wavelet
Transforms to model stationary and non-stationary trends using a 10-day forecast.
27
Epidemic modeling in the past has extensively used compartment models, especially the Susceptible -
Infective - Removed (SIR) model and its variants. Researchers in China have implemented a dynamic
Susceptible Exposed Infectious Removed (SEIR) model along with an Artificial Intelligence model to predict
the COVID-19 spread in China and identify underlying patterns in the spread.
28
A modified SEIR model
has been implemented to forecast the spread of COVID-19 and its burden on hospital care under varying
social distancing conditions.
29
The study concludes that the best parameter to assess the effectiveness of
4
. CC-BY-NC 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint
The copyright holder for thisthis version posted November 27, 2020. ; https://doi.org/10.1101/2020.11.22.20232959doi: medRxiv preprint

confinement and risk of virus diffusion, is the average number of daily contacts in a population (c).
Spatio-temporal modeling:
Researchers have focused on modeling and forecasting the spread of COVID-19 in the spatio-temporal domain
to identify not only ‘‘how much” the disease has spread but also ‘‘where” it has spread to. A study conducted
for Germany has developed a memory based integro-differential network model to predict spatio-temporal
outbreak dynamics such as number of infections, hospitalization rates, and demands on ICU due to COVID-19
at state level.
13
The model takes into account effects of different containment strategies, event and contact
restrictions, and different courses the infection may take; which is not possible when dealing with traditional
SIR models. A novel approach is taken using Moran’s I test to find regional correlations of COVID-19 cases
in China.
30, 31
The spatial association between states are modelled into the following six types:
30
1) Sharing
of borders 2) Euclidean (Shortest) distance 3) Population 4) Population density 5) Number of doctors and
hospitals 6) Number of medical beds. Results show high positive correlation between an infected region
and its adjacent regions for the first five models. This method is useful for predicting outbreaks in specific
regions, which in turn will allow policymakers to take proactive measures to prevent COVID-19 spread to
adjacent regions. The county level spread of COVID-19 in relation to the healthcare capacities in Ohio,
USA is predicted and the results show that the disease spreads much faster in counties that facilitate air
transportation to other counties.
32
This shows that similar to attributes such as population, underlying
spatio-temporal attributes such as inter-state travel can be employed to predict the dynamic spread of the
pandemic.
3.2. Modeling the impact on diverse populations
In this section, we review methods that group the population into clusters based on different factors such
as occupation, age and other such parameters and analyze COVID-19 spread in those demographics. An
overview of what characterizes the population diversity is given in Fig. 4.
The importance of this diversity/context awareness of information for handling the pandemic across the
globe has been studied.
33
Even within similar geographies, socio-economic factors have been shown to have
an effect on the spread and severity of the pandemic.
34
This work shows why the analysis, prediction and
mitigation of COVID-19 related issues should be done considering the context (diversity of the population)
for best results.
Models developed to handle diversity awareness are mostly mappings between higher dimensional spaces (in
cases where the context is passed as an input) with the capability to capture the multi-modal behavioral of
data. We see multi-variate statistics, time series models, artificial neural networks and other AI models being
used in this landscape. Key challenges in this domain are handling non-uniformly sampled data, incomplete
data and figuring out the context without it being readily fed into the models.
5
. CC-BY-NC 4.0 International licenseIt is made available under a
is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint
The copyright holder for thisthis version posted November 27, 2020. ; https://doi.org/10.1101/2020.11.22.20232959doi: medRxiv preprint

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Use of artificial intelligence on spatio-temporal data to generate insights during covid-19 pandemic: a review" ?

In this review, the authors present a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19. Furthermore, the authors catalogue the articles in these areas based on spatio-temporal modeling, intrinsic parameters, extrinsic parameters, dynamic parameters and multivariate inputs ( to ascertain the penetration of AI usage in each sub area ). It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. The authors conclude by listing potential paths of research for which AI based techniques can be used for greater impact in tackling the pandemic. 

Q2. What are the future works mentioned in the paper "Use of artificial intelligence on spatio-temporal data to generate insights during covid-19 pandemic: a review" ?

The literature survey has uncovered two key application areas in which AI is used -- predicting the future impact of COVID-19 on populations and uncovering the differential impact of COVID-19 on diverse segments of the population. Table 3 lists a few COVID-19 related issues for which AI may be an ideal candidate. 66 Due to the improvement in processing power of mobile devices, AI algorithms can be adapted to solving problems such as proximity identification and improve the accuracy and reliability of these systems through adaptation and real-time learning. Graph Neural Networks ( GNN ) can be used to model and identify specific events in the spatial and/or temporal domain. 

Q3. What are the key challenges in the analysis of COVID-19?

Key challenges in this domain are handling non-uniformly sampled data, incomplete data and figuring out the context without it being readily fed into the models. 

Q4. What are the main characteristics of the models developed to handle diversity awareness?

Models developed to handle diversity awareness are mostly mappings between higher dimensional spaces (in cases where the context is passed as an input) with the capability to capture the multi-modal behavioral of data. 

Q5. What can be done to model the spatial information of the input data?

The spatial information can be modelled through graphs and the information can be propagated within the nodes through message passing. 

Q6. How is the prediction of COVID-19 performed?

In addition, a machine learning based random forest model has been used to forecast the number of COVID-19 cases with a mean correlation coefficient R2 of 0·914.22A novel statistical method has been developed to forecast the spread of COVID-19 globally. 

Q7. What databases were used for the literature survey?

The literature survey for research papers on existing work on COVID-19 (or pandemics in general) and AI was done using IEEE Xplore, PubMed and Google Scholar for articles published between the time period 2019 and 2020 September. 

Q8. What is the amount of investment in R&D in the intersection of AI and healthcare?

A large portion of annual investment (US$ 1·7 billion in 201869) for R&D in the intersection of AI and healthcare comes from tech corporations such as Google and Facebook.70 

Q9. What are the main advantages of using deep clustering techniques?

Deep clustering techniques based on convolution neural networks have outperformed traditional methods64 for such clustering problems. 

Q10. What is the effect of the COVID-19 epidemic on the population of the affected country?

Another study developed a real-time risk assessment method for future cases in terms of fatality rate ofan affected country, using population density, healthcare resources, and government enforced preventive measures. 

Q11. What is the parameter to assess the effectiveness of COVID-19?

The study concludes that the best parameter to assess the effectiveness ofconfinement and risk of virus diffusion, is the average number of daily contacts in a population (c). 

Q12. Why are the preprints not made available to the public?

Since a major portion of the AI research and development ends up in commercial deployment, they are not made available to the public. 

Q13. What is the need for more rigorous review?

with further proliferation of articles, a need for more rigorous review has emerged, specifically, reviews that catalogue projects based on their specific utilization of AI i.e. cataloguing based on which tasks are achieved by AI and how. 

Q14. How can the authors predict the spread of COVID-19?

This shows that similar to attributes such as population, underlying spatio-temporal attributes such as inter-state travel can be employed to predict the dynamic spread of the pandemic. 

Q15. How did the researchers forecast the spread of COVID-19 in Italy?

In an attempt to solve the patient triage problem related to COVID-19, researchers in Italy have formulated a discrete time markov chain model to predict the number of COVID-19 cases, to thereby, efficiently allocate ICU resources across the country. 

Q16. What was the effect of the ARIMA model on the number of future cases?

The number of future cases was predicted using an ARIMA model, and a regression tree algorithm was used to validate the argument that the aforementioned variables have the highest impact on fatality rates in those countries. 

Q17. How many cases of COVID-19 have been estimated in India?

A data driven forecasting method has been developed to estimate the number of positive cases of COVID-19 in India for the next 30 days. 

Q18. What is the effect of climate on the spread of COVID-19?

Another similar study (using ANN based differential equations and particle swarm algorithms) shows that population density (followed by relative humidity) has a higher impact on the spread of COVID-19 in comparison to temperature and wind speed. 

Q19. What is the way to model the effect of population flow with consideration on to the geography?

Modeling the effect of population flow with consideration on to the geography has outperformed the naive population flow models in terms of accuracy. 

Q20. What is the role of data in the prevention of COVID-19?

This data is also crucial in instructing the authorities involved in policy and decision making, and will thereby ensure efficient containment of the virus spread.