Position Paper

1. Multiwave COVID-19 Prediction from Social
Awareness using Web Search and Mobility Data
Sudi Murindanyi
October 24, 2022
The coronavirus (COVID-19) has disastrously impacted society worldwide in the past
two years. Prediction models incorporating different data types are now necessary due
to COVID-19 effects. Several proposed prediction models utilized mobility information
to forecast the first outbreak wave. However, given that the evidence from the USA and
Japan demonstrated a variable correlation between infection cases and mobility patterns
over different waves, such prediction models were not appropriate for multiwave predic-
tion scenarios. The evidence increased the need for more accurate ways to forecast the
multiwave pandemic. A study using a Social Awareness-Based Graph Neural Network
(SAB-GNN) that considered the decline of symptom-related web search frequency to cap-
ture the changes in public awareness across several waves developed a unique multiwave
prediction method. Personally, the approach that included web search data in addition to
mobility data for COVID-19 multiwave prediction is supported.
First, it relied on the characteristics of the virus and societal elements, including hu-
man mobility, public knowledge, and intervention regulations. The eruption of several
waves, which suggested a cyclical return to many infection cases, is one of the important
ways that illness spreads. Therefore, it was necessary to properly utilize various forms of
data to build an accurate model that can anticipate several waves. During the first wave,
most proposed prediction models used mobility data, depicting population movements
and their relationship to infection cases. Infection and mobility statistics do not, however,
consistently correlate over time. Evidence from the USA demonstrated that human move-
ment varied. For example, evidence from the USA showed human mobility fluctuated by
around 95% from July 1 to December 1, 2020. However, the USA witnessed a second
wave in July and a third in November. Therefore, due to the complex spread patterns of
COVID-19, there was a need to use more representative data. Research using SAB-GNN
sought to bridge this gap using symptom-related web search records.
Secondly, web search records for COVID-19 symptoms, such as fever, cough, and
headache, revealed pandemic-related signs that were otherwise impossible to discern from
mobility data. For instance, there was a 0.719 Pearson correlation between high-risk cases
defined from web search records and infection cases with a 16-day lag for the second wave.
It also found that people’s symptom-related web search frequency was less during the sec-
ond wave than the first wave, though the number of patients was significantly higher. The
results above showed that web search data and human recovery awareness decay could be
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2. leveraged to predict the multiwave pandemic better. This evidence revealed a prevalent
social phenomenon in which people’s awareness of a hot topic gradually declines (re-
ferred to as ”social awareness decay”). The social awareness decay effect demonstrated
that the probability of symptom-related word searching decreased with time after the first
COVID-19 wave. The decrease in web search frequency could be explained by the fact
that people adapted to mask policies, travel restrictions, and routine tests and paid less
attention to COVID-19. Hence, the study proposed a social awareness recovery module
in the SAB-GNN to estimate the actual occurrence of COVID-19-related symptoms.
Finally, the SAB-GNN used the combined strength of graph neural network (GNN)
and long short-term memory (LSTM) to capture the Spatio-temporal disease transmission
dynamics and perform multiwave prediction. The novel neural network, the GNN, cap-
tured the multi-hop neighborhood relationships between neighborhood nodes. To execute
the disease infection case prediction in the study, It has been used to its advantage that the
GNN was better at representing a spatial dependency. Furthermore, the mobility data was
sufficient to depict the disease-spreading patterns during the first wave due to its direct
connection to the infection. In addition, an LSTM model captured the temporal relation-
ship between district-level characteristics and infection cases.
Experiments on Tokyo’s third and fourth peaks affirmed that the SAB-GNN outper-
formed other baseline models and captured the increasing trend of pandemic waves. The
underlying reason for the superior prediction of SAB-GNN included leveraging the power
of GNN and LSTM to capture the Spatio-temporal dynamic of disease spreading. In ad-
dition, web search features provided extra information as unconfirmed symptoms. The
social memory decay module properly reflected the social awareness decay as people get
accustomed to COVID-19 during the pandemic. Furthermore, except for the historical
infection and mobility data, the SAB-GNN approach utilized novel symptom-related web
search data, which provided alternative evidence of future waves. More importantly, the
social awareness decays effect and proposes the social awareness recovery module to es-
timate infection risks.
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