Metrics of Disease Preparedness

By EllieKay @ 2026-06-30T12:31 (+1)

A primer on how to tell if your country is prepared for future pandemics, and biological risks more generally. This is a cross-post from my Medium. 

🦠 Intro

🦠 How to measure preparedness?

These are the ones I’ve focussed on in this essay:

But they’re by no means the only metrics that can inform you about a country’s pandemic preparedness. Some additional, related metrics include:

 

1️⃣ States Parties Annual Report (SPAR)

❓Background

❓How is it calculated?

Figure 1. Summarising the capacities covered by the SPAR, 2nd edition. Sources: (1) (2)

❓Interesting findings

Countries in Europe tend to have the best SPAR scores across all capacities.

The best summary I could find of SPAR results was a meta-analysis by Santalucia et al, 2025. It summarises 9 studies between 2020–2024, each of which compared SPAR scores with outcomes from the COVID-19 pandemic. They found that:

“The scatter plot shows a slight indication that the better ranked countries seem to have higher mortality rates than the countries with lower ranks. However, it is important to note that the mortality data were extracted on June 1st and some of the regions had not yet peaked in terms of mortality at the time (e.g., Latin America). […]

Put another way, the countries with better preparedness did not have better health outcomes in the first wave as measured by the number of COVID-19 deaths.

For this pandemic, national level health preparedness rankings were not an indicator of how well a country handled the pandemic. In fact, we ask the question, did the health rankings lead to a false sense of confidence among countries or was COVID-19 impossible to prepare for, is the COVID-19 pandemic much different from previous pandemics?” — Kachali et al, 2022

Another report analysed in the meta-analysis by Santalucia et al was Satria and Tsai 2022, which looked at improvement in SPAR scores between 2019 — 2020 across 154 countries.

Satria and Tsai went on to write a 2025 paper further discussing the limitations of the SPAR metric.

Echoing some of these aforementioned criticisms, a 2022 article by Sakiko Fukuda-Parr criticised the SPAR for not actually helping countries to prepare for pandemics in the long term.

“this indicator has generated few discursive or policy consequences. It appears to be immune to challenge even from the experience of COVID-19” […]

❓Relevant papers

 

2️⃣ Joint External Evaluation (JEE) tools

❓Background

❓How is it calculated?

Figure 2. The JEE core capacities and indicators. Source: (1)

❓Interesting findings

I found an article by Jain et al in 2022 summarises the limitations of the JEE pretty well…

“While universal health coverage indices (β -0.04 p<0.001) and international tourist arrivals were associated with log communicable disease deaths (β 0.02, p = 0.002), they were not associated with log COVID-19 deaths.

Although the same tool is used to assess capacities for both epidemics and pandemics, the JEE may be better suited to small outbreaks of known diseases, compared to pandemics of unknown pathogens.” — Jain et al in 2022

Lee et al, 2024 further evaluated the predictive accuracy of the JEE.

Figure 3. There was no statistically significant relationship between any of the JEE score categories and COVID-19 fatality rate. There was a significant relationship between some of the categories and infections per million / deaths per million, though. P-values are Table 2 in Lee at al, 2024.

❓Relevant papers

3️⃣ Global Health Security (GHS) Index

❓Background

❓How is it calculated?

The GHS rates the following across 195 countries (see Figure 4 and Figure 5 below for more details on each category):

For all categories, countries are scored between 0 to 100 where 100 is the most favourable score.

❓Interesting findings

Figure 4. Table listing the top 10 countries with the highest ‘overall’ GHS scores, per the 2019 GHS report. I also added the number of COVID deaths per million, as reported by ‘Our World in Data’. In the last column I added each country’s population density per Km² (not weighted), just to provide a rough idea of population clustering.

Figure 5. Summary of the metrics examined, and some findings for each category in the GHS 2021 report.

The GHS 2019 report highlighted Thailand as a particularly good example of a low-and-middle-income country that was pandemic-prepared — being the only non-high-income country that had an overall score above 66.7 / 100.

❓Relevant papers

4️⃣ Dynamic Preparedness Metric

❓Background

❓How is it calculated?

The DPM framework assesses the preparedness of countries to five specific disease syndromes:

Preparedness is quantified using three dimensions, measured from 1 to 5 where 5 represents the lowest risk (see Figure 6 below for more detail):

“The DPM is a composite measure that provides preparedness risks for five syndromes for all 196 State Parties […] using multisector open-source data to perform up-to-date contextual assessments.”

“The potential pathways of spread and the impact on society for each syndrome are heterogeneous; thus, the preparedness capacities and actions needed to contain each syndrome are specific.”

Figure 6. Taken from the Dynamic Preparedness Metric methodology report by the WHO, figure 3 on page 6.

❓Interesting findings

A 2025 analysis by Vernaccini et al examined the DPM indices across 196 States Parties, across the following six WHO regions (see Figure 7 and Figure 8 below):

At a high level, their analysis indicated that 46% of the world’s population is at risk of health emergencies across all syndromes covered in the DPM index.

Africa (AFR) had the largest percentage (57%) of the population at risk, while South-East Asia (SEAR) had the largest absolute number of people at risk (1.1 billion, 51%).

The syndromes of greatest risk for each region were:

The American (AMR) and Western Pacific (WPR) regions showed the greatest variability in hazard scores.

“in [The Americas], 25 countries (71%) had a score of four or above for diarrhoeal syndromes, yet only 18 (51%) had the same score for neurological syndromes.”

Most countries (93%) in Europe (EUR) saw a decrease in their DPM scores (i.e increased risk) between 2018–2021.

In EUR, the main risk factors are related to urbanization, the elderly population with comorbidities, and international movements.

Figure 7. Different WHO regions and the % that have a Preparedness Capacity Gap (PCG). This metric combines the three DPM dimensions. The difference between a country’s capacity and threat is the PCG, where negative values correspond to a preparedness gap. Table 2 in Vernaccini et al.

 

Figure 8. Breakdown of the DPM scores for countries, aggregated per WHO region. Taken from Figure 1 in Vernaccini et al.

Figure 9. Breakdown of DPM scores, by syndrome, for different WHO regions. Taken from Figure 2 in Vernaccini et al.

❓Relevant papers

2022 paper — Dynamic preparedness metric: a paradigm shift to measure and act on preparedness

2024 WHO report — Dynamic preparedness metric: methodology report

2025 paper — The dynamic preparedness metric: results from a global and regional analysis of health emergency preparedness

 

🦠 My personal main takeaways…

 

🦠 Footnotes

  1. ^

    Composite metrics are not always adequate metrics for predicting how well countries will handle pandemics.

    One major reason for this is because these metrics don’t adequately measure social and cultural factors. And as we saw with COVID, social dynamics play a big role in pandemic management.

    You can have the best experts and systems in the world…but if your population isn’t complying, there’s only so much you can do to prevent a pandemic from spreading further.

    For example, the paper “Are preparedness indices reflective of pandemic preparedness? A COVID-19 reality check” by Kachali et al in 2022 compared:

    (1) how well countries were ranked in terms of the IHR and GHSI benchmarks

    and

    (2) how they actually performed in the first wave of COVID (cumulative reported deaths per million in the first 60 days).

    They found that “the countries with better preparedness did not have better health outcomes in the first wave as measured by the number of COVID-19 deaths”.

    Ultimately, metrics of pandemic preparedness are only as good as what they can measure. Since factors like social dynamics are hard to capture, it’s hard to predict how a country will cope with a pandemic in practice.

  2. ^

    The “1st Edition” (2018) of the SPAR self-assessment questionnaire looked at 13 core capacities and 24 indicators.

  3. ^

    As far as I know, they looked at reported fatality rates and not excess deaths. Maybe it’d be worth coming back to this later, and seeing whether there’s a relationship between the SPAR, JEE, GHS, and DPM scores and excess deaths during / after COVID.

  4. ^

    In terms of economic recovery, it looks like the COVID Economic Recovery Index (CERI) also suggests that Thailand did well. It had an overall economic resilience score of 56.23 / 100 — placing it at number 44 / 122 in the overall rankings, above countries that have over 3x its GDP per capita — like Greece, Bulgaria, and Croatia.

    Thailand’s financial system resilience score in the CERI rankings, specifically, was 84.07 / 100, placing it above countries like New Zealand, Austria, Korea, the Netherlands, the UAE, France, Spain, Luxembourg, and many others for that metric.

    CERI index row for Thailand (https://www.covidrecoveryindex.org/copy-of-rank-health-resilience)