Winter 2024: Cyclone Yaku and Dengue

Following lab meeting, I am working through several suggestions. The first key one is to redefine the treatment group and control pool to sensibly include Ecuador. Below, I show a new set of treatment groups that includes three regions in Peru and three additional provinces in Ecuador. This group was constructed by setting a threshold of anomalous precipitation during Yaku (> 90th percentile, (see Figure 1C) and generally experiencing a warm climate that is not wet (first or second tercile for precipitation and not cold (second or third tercile for temperature in Figure 1A). Note that the two northernmost treated provinces in Ecuador are in the second tercile for precipitation and we could instead exclude them to focus on places that are particularly dry in general. Without the temperature threshold, a random additional province in the middle of Ecuador that is cool and dry becomes part of the synthetic control.

The tercile cutoffs for rainfall (mean mm per day) are: 5.4, 47.76 and the tercile cutoffs for temperature are 12.7 and 21.3 C. So our threshold is below 47.76 mm per day and above 12.7 C.

Figure 1: Climate conditions across regions in Peru and provinces in Ecuador from 1993 - 2023 compared to during Cyclone Yaku. In all panels, the bolded outline encompasses locations included in the synthetic control. (A) A bivariate plot where locations are shaded according to mean temperature and mean precipitation compared to other regions from 1993 - 2023. Redder tones indicate warmer locations and bluer tones indicate locations with more precipitation. (B) Locations are shaded according to the total amount of precipitation received during Cyclone Yaku, from March 7th - March 20th, 2023. (C) Locations are shaded according to the percentile for biweekly cumulative precipitation experienced during Cyclone Yaku, compared to the distribution of biweekly cumulative precipitation from 1993 - 2023. Darker shades indicate locations that experienced more precipitation than usual during Cyclone Yaku.

Cases were also relatively high across the treated provinces following Cyclone Yaku.

Figure 2: weekly dengue incidence across provinces in Peru from 2010 - 2023 compared to the 2023 outbreak. In all panels, the bolded outline encompasses the group of northwestern provinces most affected by Cyclone Yaku, used as the treatment group in the synthetic control analysis. Provinces in white had no reported dengue from 2010 - 2023. (A) Provinces are shaded according to their mean weekly dengue incidence across the time series. (B) Provinces are shaded according to their mean weekly dengue incidence in the weeks following Cyclone Yaku (week 10 - 26 of 2023). (C) A bivariate plot where provinces are shaded according to their precipitation anomaly during Cyclone Yaku and dengue incidence in the weeks following the cyclone. More pink tones indicate provinces with greater dengue incidence and more blue tones indicate provinces with a greater anomaly in precipitation (e.g., high precipitation compared to their typical weather).

Here, we demonstrate using a synthetic control method to evaluate the impact of the cyclone on the treated provinces (those impacted by the cyclone) compared to the synthetic control, a set of untreated provinces selected by matching on dengue cases (logged), temperature, and precipitation prior to Cyclone Yaku and logged population size (in 2020), aggregating weekly variables to four-week periods. The pool of untreated units used to construct the synthetic control includes locations in Mexico, Brazil, Ecuador, and Colombia.To avoid including provinces that also may have been impacted by Yaku, we remove provinces that experienced precipitation above their 80th percentile during the biweek of cyclone Yaku.

The synthetic control is able to match most of the outbreaks in the treated provinces, particularly since 2020 (Figure 3A). The synthetic control is generally a bit cooler than the treated provinces, which is generally hotter than the mean temperature across all locations in the untreated pool (Figure 3B). Similarly, the synthetic control generally matches the relative dryness of the treated provinces, although there is a large mismatch in 2017 when synthetic control was considerably more dry than the treated provinces (Figure 3C). The treated units have slightly more cases following Cyclone Yaku compared to the synthetic control, but this difference is within the range of the placebo differences 3D).

Figure 3: Synthetic control results where three treated Peru regions were matched to locations in Peru, Colombia, Mexico, Ecuador, and Brazil. (A) Dengue incidence aggregated to four-week periods from 2010 - 2023 across the treated provinces (black), the synthetic control (maroon), and all untreated province (grey). The dashed vertical line indicates when Cyclone Yaku occured. Panels B and C similarly display temperature and precipitation over time in the treated, synthetic control, and all untreated province. (D) The difference in cases between the treated and synthetic control provinces over time. Values above the dashed vertical line indicate greater incidence in the treated compared to synthetic control provinces. Each grey line is the result of a permutation test for comparison. Panels E and F similarly display the difference in temperature and precipitation over time. (G) Weighting of provinces in constructing the synthetic control. Treated provinces are indicated in pink. Excluded provinces are indicated in grey. [NOTE: I know the color scheme is bad for now, allows more flexibility until we have actual results]

Figure 4: As above, but only matching from 2018 onward based on Emma’s suggestion.

I think part of the issue here is that it might not make sense to aggregate climate variables like this. To get a better sense of our data, here cases, temperature, and rainfall in each of the synthetic control locations plotted separately, plus the mean value that’s used for the synthetic control.

Figure 5: Value of each province in the treated group is plotted in a different color. The mean value across all provinces is plotted in black. Below that is a table of each location and its population size.

## # A tibble: 6 × 2
##   name      pop
##   <chr>   <dbl>
## 1 EC13  1594995
## 2 EC20    32721
## 3 EC24   403364
## 4 PE14  1341767
## 5 PE20  2033620
## 6 PE24   255728