How Brazil Scans Dengue and Chikungunya Outbreaks ✛

Original: "Large-scale Epidemiological modeling: Scanning for Mosquito-Borne Diseases Spatio-temporal Patterns in Brazil"
arXiv:2407.21286 · 2024-07-31 · CC BY 4.0 · 1 min
Over 14 years, data on 18,567 dengue epidemics and 2,199 chikungunya epidemics were collected across all 5,570 municipalities of Brazil.

A fisherman casts his net into murky water and pulls out fish. That's roughly how Episcanner works: it sifts through 14 years of disease records and hauls out every fever outbreak in every municipality in Brazil. From 2010 to 2023, the net caught 18,567 dengue epidemics and 2,199 chikungunya epidemics. Previously, such data were collected piecemeal, making it hard to compare north and south. Now all the fish are in one boat.

Each epidemic gets a passport: how many people one patient infects, when the peak hits, how long the illness lasts. For dengue, the average epidemic lasts 26.6 weeks, which is more than half a year. Chikungunya is a bit shorter at 25.5 weeks. But those are national averages; the real picture is as varied as fish scales.

In northern Brazil, dengue epidemics last longer than in the south. In some northern states, an outbreak can drag on for nearly a year, while in the south it may fizzle out within a couple of months.

Why is that? Epidemics are spread by mosquitoes, whose activity depends on temperature and rainfall. When the ocean is warm (El Niño), epidemics arrive earlier. When the ocean is cool (La Niña), they come later. A warm ocean seems to spur mosquitoes on.

Now health authorities have an open dataset, and they can plan where and when to set mosquito traps or stockpile medicines. The fisherman isn't just showing off his catch; he's pointing out where in the river to cast the net next time.

The average duration of a dengue epidemic in Brazil is about 27 weeks, which is more than half a year. In northern states, epidemics can last nearly a year, while in the south they may last only a couple of months.

J(t) = L - L[1 + \alpha e^{b(t-t_j)}]^{-1/\alpha}
Describes the growth of cases during an epidemic; L is the final size, t_j is the peak week, alpha and b are shape parameters.
R_0 = \frac{1}{1-\alpha}
Shows how R0, the average number of infections caused by one infected person, is derived from the shape of the epidemic curve.
Concepts
epidemic model epidemic Multivariate ENSO Index Niño 4 spring predictability barrier ENSO IRI El Niño index interannual variability