The University of Basel COVID-19 epidemiological simulation tool

While the Imperial College projection results are easy to get, it might be useful to rely on more current and country-specific parameters, especially for hospital and ICU capacity as well as exploring further scenarios and variations for other parameters. The epidemiological simulation tool developed at the University of Basel offers this possibility. I found it to be quite intuitive and easy to use. It comes with preloaded values for many OECD countries and regions, including the current number of reported cases and deaths allowing comparisons between projections and current numbers. At this stage, the only developing country with preloaded data is India (and each Indian state). But the tool can still be customized for other countries. First, it is possible to contribute new data to their platform. Next, the age distribution for most countries is preloaded (set the scenario to Custom and pick the relevant country under Age Structure). Finally, users will have to input a few country-specific parameters (e.g. estimated hospital beds, estimated ICU/ICMU beds, etc.), but this should allow for more precise and tailor-made projections.

The tool doesn’t directly take into account the difference in social mixing patterns across countries, but this could be modeled by varying the initial values in the mitigation scenarios interface.

What the University of Basel tool allows is to introduce seasonality and the impact of different climates. This doesn’t seem to be integrated in the Imperial College projections. A word of caution is important here: while there seems to be emerging evidence that higher temperatures and humidity levels reduce the transmission of the virus (see this paper by Wang et al (2020) for estimates based on a set of cities in China), the evidence is too preliminary to conclude that most developing countries’ experience will be milder because of their weather patterns.

It is important to also note that both the Imperial College results and the University of Basel tool assume no substantive difference in general health and pre-existing conditions prevalence between Chinese and other populations, an assumption that is unlikely to be validated in practice. Moreover, the standard of medical care available varies significantly across the world and tends to be substantially lower in many developing countries, especially among the poor (see Das, Hammer and Leonard, 2008; Kruk et al. 2018). The impact of a lack of adequate care for more severe cases of COVID-19 is difficult to quantify but it is likely to significantly increase overall mortality and could be compounded if the number of cases requiring care leads to a disruption of the health system.

As of now, there are a few other COVID-19 epidemiological simulation tools available, such as this one by Alison Hill and colleagues and this one by Gabriel Goh. I am less familiar with them and they do not seem to include pre-loaded country-specific data such as the important age structure of the population. The Institute for Health Metrics and Evaluation (IHME) just released projections for the United States and its 50 states, based not on an SIR model but focused on modeling the empirically observed COVID-19 population death rate curves. However, it seems to only consider a scenario under which physical distancing measures are maintained.

If you know of other projections and tools, please mention them in the comment section. Suggestions and comments are welcome: this is a very rapidly changing landscape with new information and data coming out on a daily basis.

Let me finish with words of wisdom from the American humorist Evan Esar: "An economist is an expert who will know tomorrow why the things he predicted yesterday didn't happen today." Very little is known yet about COVID-19, a disease that broke out last November. Key parameters such as the transmission rate and the infection fatality ratio are estimated based on sparse data. Several drug and vaccine trials are underway and will hopefully help mitigate the impact of this pandemic, but in the meantime, countries need to prepare, and try to anticipate, to the best of their ability, what is coming their way.

This post benefited from discussions with and suggestions from World Bank colleagues Massimiliano Calli, Gabriel Demombynes, Patrick Hoang-Vu Eozenou, Jed Friedman, Eeshani Kandpal, Aart Kraay and Aaditya Mattoo. All errors are mine.