Chapter 29

Pandemics

The Covid-19 pandemic has thrown bioinformatics into prominence, but dealing

with the pandemic has mainly required existing tools. It has provided an unprece-

dented volume of sequence data, which should ultimately lead to a much profounder

understanding of the evolution of SARS-CoV-2, and maybe other viruses as well.

The primary intersection of bioinformatics with Covid-19 is sequencing. The

assiduous monitoring of Covid cases has allowed the prompt detection of mutants.

Goals of sequence analysis include the impact of mutations on structure, antigenicity

and transmissibility. There has, however, been such an abundance of data from health

laboratories and hospitals that there has been no real need for computational predic-

tion of these impacts—they have rapidly been observed directly. Perhaps the most

useful contribution of computational prediction of variant phenotype is to indicate

when a variant might pose a significantly elevated public health risk—prior to any

actual manifestation of that risk—and enable a preemptive public health response.

For example, a highly transmissible variant could warrant a lockdown sufficiently

restrictive to ensure that the “basic reproduction number” upper R 0R0 (cf. Eq. 20.7) ¹ is less

then 1. ² Public health responses are often guided by modeling the epidemic, with

which the consequences of choices available to public authorities, such as lock-

down or vaccination, can be demonstrated. ³ Thomas (2020) quotes Finkelstein on

1upper R 0R0 is the number of people in a fully susceptible population who will be infected by an average

person carrying the infection before that person recovers—it is thus a good measure of how well

people are physically (“socially”) distancing; indeed it is sometimes called the social distancing

index (SDI). Then there is the R-rate (upper RR or sometimes upper R Subscript normal eRe) or effective reproduction number,

which was prominently used by the UK government; it equalsupper R 0R0 multiplied by the fraction of the

population who are susceptible.

2 Early Wuhan data showed it to be 2.35 in an unrestricted situation (Kucharski et al. (2020).

3 E.g., Thomas (2022).

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J. Ramsden, Bioinformatics, Computational Biology,

https://doi.org/10.1007/978-3-030-45607-8_29

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