Hi everyone. I am Sreelekha Guggilam and I am here to talk about identifying anomalous COVID-19
trends and impact of different COVID-19 policies on these trends using large deviations theory.
Now before we dive into the topic, let's discuss why do we need to study anomalies or anomalous
cover trends in the first place. So, well, in most contexts, anomalies can be either the worst or the
best possible outcomes because they're very often rare, and learning more about them can
help us understand troubleshoot the issues or in case if beneficial, seek methods to reproduce these
beneficial anomalies when necessary. Especially in case of epidemiological studies one can study
the policies that failed or succeeded in controlling spread of diseases like COVID.
However the epidemiological data is often large, high dimensional, and temporally evolving with too
many external latent factors, which makes them very hard to study. For instance, look at the COVID-19
confirmed cases trend. As you can see, multiple policies locally and globally were implemented
and relaxed that resulted in evolving trends. Due to the complexity associated with the data,
analyzing them is difficult using traditional methods. In this figure we present a temporal
anomalous core in the shaded blue around the trend line in Italy. The wider the shade is, the
more anomalous the trend is as compared to the global patterns. But why do we need to use a novel
method instead of traditional approaches? To study temporal data like COVID data,
there are multiple models like time series modeling algorithms, epidemiological models, as well
as for curve fitting algorithms. However, most often these algorithms cannot handle multivariate data
like in the case of time series models like RMR or sarima, or cannot detect evolution over the time.
This is why we present our LAD model. The model is designed to capture anomalies
in high dimensional data without the use of dimensionality reduction. It's based on large
deviation theory and is capable of capturing change in trend across different features.
The model relies on the Dawson-Gartner Projective LDP principle to deduce anomalies probabilities
in high dimensional data. Due to this
we can capture both the temporal and lateral dependencies within the time series database.
The detailed theoretical details of the model as well as the background on the last deviations
is omitted in the interest of time. For more details one can refer the people listed below.
In this section we'll look at anomalous COVID trends in U.S. counties, across various countries globally,
as well as we study the impact of different policy changes in U.S. The data sources are listed here.
To study anomalous COVID trends in us counties multiple settings were tested. Here we only present
the results based on complete time series, history versus considering only the instantaneous time
step. For evaluation, total confirmed counts and death counts were collectively studied.
Based on the setting different set of anomalous counties have been identified.
One particular point of interest is the counties that have been identified as the anomalous in
the full history setting. In particular it can be seen that increasing trends in North
Dakota counties were observed after the Sturgis Motorbike Rally in North Dakota during COVID.
Additionally, removal of face mask mandate in Florida was followed by the spike in Miami County,
which is seen in figure 3A and 3B.
Similarly, most counties in New York have been pegged as
anomalous during the initial series of pandemic which was called captured in figure 3C and 3D.
To study the global trends we look at the total confirmed and death counts,
daily new cases, bi-weekly growth rates, and case fatality rates collectively.
The most anomalous trends were observed for Italy, US, UK, and Mexico countries. For the
sake of analysis, only countries with greater than 10 million population were considered.
Of particular interest as the anomalies score for the Mexico in the late 2020. This is due
to the high fatality rates as compared to the global normal as seen in the following slide.
Also the high anomaly scores for the countries in March
to May 2020 is seen due to high bi-weekly growth rates are seen in figures 6 and
6. Even though anomaly scores are seen to change, the reason behind these increase
in trends must be further studied, thus we look at policies implemented in different countries.
Here we study the US policies implemented over time and the delayed impact they have on
the covet 19 daily confirm counts. In particular it can be seen that the high normally scores in
April 2020 were followed by multiple lockdown, travel bans, testings and face covering policies,
which eventually brought down the spread as compared to the global normal.
However, relaxation of the workplace closure and stay at home in early fall 2020 followed by the
travelling during Thanksgiving and spring and Christmas breaks led to increase in covid 19
spread as seen in figure 8. Similar trends can be actually seen in death counts in figure 9 here.
There are a lot of future works that are associated with the model, for instance the
measure of impact of the policy implementation as well as the delay are still to be studied.
Weighted scores can be actually implemented for features as well as temporarily,
prioritizing more recent events. Clustering similar trends and studying anomalies
in multi-modal data sets can also be useful. Thank you.