Israel’s COVID-19 Outbreak — Looking for COVID City Clusters
By: Dina Daitchman
Tags: Covid 19, Clustering, Data Science, Data Visualization

Israel’s population density and diversity make it a great case study to help people better understand the pandemic and learn ways to combat its effects. With an abundance of data to help people understand the virus’s patterns, fluctuations, and infected areas, there is much to be learned. Below we will try to compare different cities in Israel. Intuitively, if two cities had similar behaviour in the past — we might learn to predict outbreaks in the future.

In May newspaper headlines in Israel asked a fateful question, “Has Israel defeated the pandemic?” It was a question that those same headlines and the public answered by declaring success. Today, we’re left looking back ruefully at the premature declarations of victory, but we’re also left with a treasure trove of valuable data. Contrary to the monolithic narratives of victory or failure playing out in popular media, the data paints a varied and surprising picture related to the outbreak’s behaviour in different cities, its relation to individuals’ behaviour, and the wave of Israelis returning from abroad.

Diversity for Deep Insights

Far from the uniform entity portrayed, Israel is a dizzying array of diversity, a patchwork of distinct religious and ethnic communities and widely varying socioeconomic statuses. That diversity, combined with Israel’s population density, it is amongst the most densely populated countries in the OECD, make it an informative test case. Israel is a unique and valuable testing ground because in various ways the country has third world density and diversity but first world medicine and technology to combat the virus spread with maximum efficiency.

In this exploratory study we asked which different cities had similar COVID-19 development. If two cities had similar behaviour in the past — we might learn to predict outbreaks in the future. Moreover, it is interesting to understand why two cities that are not close to each other had an outbreak at the same time.

Here’s the Data

The Israeli Mistry of Health publishes the daily number of new cases in every neighbourhood.

Snapshot of Covid-19 daily cases by neighbourhood, published by the Israeli Mistry of Health

The fine print:

Neighbourhoods with population size less than 2000 are excluded

In case where number of cases is smaller than 15 — the value is ‘<15’ instead of the exact value.

This data can be joined and enriched by data from the Central Bureau of Statistics, where one can find the population size and various parameters for each neighbourhood are listed.

Snapshot of a subset of features provided by the Central Bureau of Statistics for each neighbourhood

Preparing the Data

The data is very sparse — 88% of the values are 0, meaning that there were no new cases in a neighbourhood on that day. To smooth the data 2 aggregations were used, which reduced the % of zero values to 57%:

Aggregation by time — taking the sum of new cases during the previous week. Aggregation by location — combining all neighbourhoods of a city.

Looking at all of Israel First

Israel had 2 COVID-19 waves, the first started in mid-March and was ‘taken down’ by a total lockdown that lasted until May. Then began the second wave, that rose above the first one. The Israeli government is trying to fight the spread of the virus without full lockdown this time.

New cases last week vs. date — where the 2 waves are clearly visible

To compare different cities we used the ‘Basic reproduction number’ (R), instead of the total number of cases. Intuitively, it is the average number of individuals infected by each positive case. It is not a biological constant for a pathogen as it is also affected by other factors such as environmental conditions and the behaviour of the infected population.

log R = log ( number of weekly cases / number of weekly cases a week ago)

basic reproduction number vs. date

We can see that although the first wave was lower, it was more aggressive than the second one. There are several behavioural reasons why the first wave was more aggressive, one is people returning from abroad and the other is the lack of awareness and no social distancing in the first critical weeks .

Snapshot of the processed aggregated data

Clustering Cities

The basic reproduction number is not dependent on population size, so we can use it to compare different cities in Israel.

Distribution of log R values in Israeli cities

Here is an example for a sample of nine cities in Israel:

Basic reproduction number (R) vs. date for a sample of nine cities

Because the two waves are very different, it was decided to focus on the second wave, which started in June.

To look for possible clusters, one can examine correlation between reproduction numbers over time across different cities. The heatmap shows a hierarchical clustering of the correlation vectors, and possible clusters are visible along the main diagonal

Correlation map between cities based on reproduction numbers over time

The correlation map between cities based on reproduction numbers over time. The heatmap shows a hierarchical clustering of the correlation vectors, and possible clusters are visible along the main diagonal.

Clustering the daily basic reproduction numbers of different cities using K-Means (k=6) showed that there are different clusters of COVID-19 development

All member cities of a cluster can be reviewed separately, for example:

Lastly, we plotted city cluster data on the map, colouring every cluster in a different colour

Israel map with city polygons, colours represent different clusters

What’s Next?

This initial study shows that there are clusters of cities in Israel that had similar COVID-19 progression, indicating that if the links between these clusters is better understood, we can more effectively curtail COVID’s spread. It is vitally important to continue in this direction and to understand why? Why some close cities behaved differently while other cities far away with seemingly little connection behaved similarly. The explanation can arise from both behavioural data like social distancing, super spreader behaviour and cross city movement from demographical data like population density, socioeconomic status, age distribution etc.

After crunching the data on Israel’s COVID-19 outbreak a number of questions remain — how is behaviour in one cluster correlated to clusters in cities that are further afield? How does age distribution influence the outbreaks? And how does travel add to its spread?

Despite these unanswered questions, one thing is abundantly clear, reactive policies to government data alone is insufficient to effectively stem outbreaks without draconian lockdowns and lives lost. It yields insights and brings up plenty of questions, but it doesn’t take into account the critically important data necessary to answer the very questions it brings to the fore. So how should decision makers proceed? The data suggests two main answers.

The first answer is by taking immediate pre-emptive action targeting a city’s specific behaviour (i.e. how the population behaved in the past). In other words, recognising that if cities showed a particular worrying behaviour during previous iterations of the outbreak it could mean that the same behaviours will be expressed in the future. To remedy that issue, decision makers can choose to increase awareness, communication, public safety activities and enforcement in cities that belong to the same cluster but haven’t yet seen a dramatic rise.

The second answer isn’t mutually exclusive with regard to the first and it entails expanding the types of data collected to better understand the relationships between cities in the same cluster. If policymakers can identify that it’s overwhelmingly young people working in a particular industrial area that is responsible for a related outbreak they can be far more effective and less disruptive in their actions.

There is no absolute truth when it comes to combating COVID-19, however preemptive data informed decisions can go a long way in returning a sense of normalcy and safety to our lives.