Variation of malaria dynamics and its relationship to climate in western Kenya during 2008-2019: a wavelet approach

Reviewer #1 (Public review):

Summary:

This study investigates the relationship between climate variables and malaria incidence from monthly records, for rainfall, temperature, and a measure of ENSO, in a lowland region of Kenya in East Africa. Wavelet analyses show significant variability at the seasonal scale at the 6-month scale with some variation in its signal over time, and some additional variability at the 12-month scale for some variables. As conducted, the analyses show weak (non-significant) signals at the interannual time scales (longer than seasonal). Cross-wavelet analysis also highlights the 6-month scale and the association of malaria and climate variables at that scale, with some signal at 12 months, reflecting the role of climate in seasonality. Evidence is presented for some small changes in the lags of the response of malaria to the seasonal climate drivers over time.

Strengths:

Although there have been many studies of climate drivers of malaria dynamics in East Africa, these analyses have been largely focused on highlands where these drivers are expected to exhibit the strongest signal of association with disease burden at interannual and longer time scales. It is therefore of interest to take advantage of a relatively long time series of cases to examine the role of climate variables in more endemic malaria in lowlands.

Weaknesses:

(1) Major comments:

The work is not sufficiently placed in the context of what is known about climate variability in East Africa, and the role of climate variables in the temporal variation of malaria cases in this region. This context includes the relationship between large (global/regional) drivers of interannual climate variability such as ENSO (and the Indian Ocean Dipole) and local temporal patterns in rainfall and temperature. There is for example literature on the influence of those drivers and the short and long rains in East Africa. That is, phenomena such as ENSO would influence malaria through those local climate variables. This context should be considered when formulating and interpreting the analyses.

There are conceptual problems with the design of the analyses which can limit the findings on association. It is not surprising that rainfall would exhibit a clear association at seasonal scales. It is nevertheless valuable to confirm this as the authors have done and to examine the faster than 12-month scale, given the typical pattern of two rainfall seasons in this area. However, the results on temperature are less clear. If rainfall is the main limiting factor for the transmission season, the temperature variation that would matter can be during the rainy periods. One would then see an association with temperature only in particular windows of time during the year, when rainfall is sufficient (see for example, Rodo et al. Nat. Commun. 2022, for this finding in a highland region of Ethiopia). For this situation, there would be no clear association with temperature when all months are considered, and one would not find a significant relationship (or a lagged one) between peak times in this climate factor and malaria's seasonal cases. It would be difficult for the wavelet analysis to reveal such an effect. Another consideration is whether to use an ENSO variable that includes seasonality or to use an ENSO index computed as an anomaly, to focus on interannual variability. That is, it is most relevant to consider how ENSO influences time scales of variation longer than seasonal (the multiannual variation in seasonal epidemics) and for this purpose, one would typically rely on an anomaly. This choice would better enable one to see whether there is a role of ENSO at interannual time scales. It would also make sense to analyze with cross-wavelets the effect of ENSO on local climate factors, temperature, and rainfall, and not only on malaria. This would allow us to establish evidence for a chain of causality, from a global driver of interannual variability to local climate variability to malaria incidence.

The multiresolution analysis and associated analysis of lag variations were confusing and difficult to follow as presented: (1) the lags chosen by the multiresolution analysis do not match the phase differences of the cross-wavelet analysis if I followed what was presented. On page 8, phase differences are expressed in months. I do not understand then the following statements on page 9: "The phase differences obtained by the cross-wavelet transforms were turned into lags, allowing us to plot the evolution of the lags over time". The resulting lags in Figure 6 are shorter than the phase differences provided in the text on page 8. (2) The phase difference of the cross-wavelet analyses for malaria and temperature is also too long for this climate factor to explain an effect on the vector and then on the disease. (3) In Table 3, the regression results that are highlighted are those for Land Surface Temperatures (LST) and ENSO, with a weak but significant negative linear correlation, and for LST and bednet coverage, and this is considered part of the lag analysis. The previous text and analyses up to that point do not seem to consider the relationship of ENSO and local climate variables, or that between local climate variables and bednets (which would benefit from some context for the causal pathways this would reflect).

The conclusion in the Abstract: "Our study underlines the importance of considering long-term time scales when assessing malaria dynamics. The presented wavelet approach could be applicable to other infectious diseases" needs to be reformulated. The use of "long-term" time scales for those of ENSO and interannual variability is not consistent with the climate literature, where long-term could be interpreted as decadal and longer. The time scales beyond those of seasonality, especially those of climate variability, have been addressed in many malaria studies. It is not compelling to have the significance of this study be the importance of considering those time scales. This is not new. I recommend focusing on what has been done for lowland malaria and endemic regions (for example, in Laneri et al. PNAS 2015) as there has been less work for those regions than for seasonal epidemic ones of low transmission (e.g. altitude fringes and desert ones, e.g. Laneri et al. PloS Comp. Biol. 2010; Roy et al. Mal. J. 2015). Also, wavelet analyses have been used extensively by now to consider the association of climate variables and infectious diseases at multiple time scales. There is here an additional component of the analysis but the decomposition that underlies the linear regressions is also not that new, as decompositions of time series have been used before in this area. In summary, I recommend a more appropriate and compelling conclusion on what was learned about malaria at this location and what it may tell us about other, similar, locations, but not malaria dynamics everywhere.

The conversion from monthly cases to monthly incidence needs a better explanation of the Methods, rather than a referral to another paper. This is a key aspect of the data. It may be useful to plot the monthly time series of both variables in the Supplement, for comparison.

There is plenty of evidence of the seasonal role of rainfall on malaria's seasonality in many regions. The literature cited here to support this well-known association is quite limited. It would be useful to provide a context that better reflects the literature and some context for the environmental conditions of this lowland region that would explain the dominant role of rainfall on malaria seasonality. Two papers (from 2017 and 2019) are cited in the second paragraph of the introduction as showing that "key climatic factors are rainfall and temperatures". This is a misrepresentation of the field. That these factors matter to malaria in general has been known for a very long time given that the vectors are mosquitoes, and the cited studies are particular ones that examine the mechanistic basis of this link for modeling purposes. Either these papers are presented as examples, with a more accurate description of what they add to the earlier literature or earlier literature should be acknowledged. Also, what has been much less studied is the role of these variables at interannual time scales, as potentially mediating the effects of global drivers in teleconnections.

(2) Minor comments:

In relation to the conceptual issues raised above, it would be valuable to consider whether the negative association with temperature persists if one considers mean temperature during the rainy seasons only, against the total cases in the transmission season each year (as in Rodó et al. 2021). This would allow one to disentangle whether the negative association reflects a robust result or an artifact of an interaction between temperature and rainfall so that the former matters when the latter is permissive for transmission.

The conclusion in the Discussion " This suggests that minor climate variations have a limited impact on malaria incidence at shorter time scales, whereas climatic trends may play a more substantial role in shaping long-term malaria dynamics" is unsubstantiated. There is no clear result in the paper on climatic trends that I can see.

The Abstract writes: "The true impact of climate change...". This paper is not about climate change but about climate seasonality and variability. This text needs to be changed to make it consistent with the content of the paper.

Page 2, Introduction: The statement on Pascual et al. 2008 is not completely accurate. This paper shows an interplay of climate variability and disease dynamics, but not cycles that are completely independent of climate.

Page 2, next sentence: "More recently, such cycles have been attributed to global climate drivers such as ENSO (Cazelles et al., 2023)". This writing is also somewhat unclear. Are you referring to the cycles for the same location in Kenya? Or generically, to the interannual variability of malaria?

There are multiple places in the writing that could be edited.

Reviewer #2 (Public review):

Summary:

The analyses of long-time malaria series to investigate the complex relationship between malaria incidence and climate is hampered by the non-stationarity introduced by both changing control interventions and irregular climate events such as the el nino Southern Oscillation (ENSO).

Strengths:

By applying wavelets the authors were able to investigate the effect of the major climate factors such as rainfall, air and land temperature, and sea surface temperature (as a measure for ENSO) while at the same time taking into account changing bednet coverage. The wavelet approach is both flexible and powerful and was able to demonstrate well that shorter term. seasonal fluctuation in malaria incidence in Western Kenya is driven by rainfall patterns, while providing some evidence for temperature and SST may predict fluctuations at longer timescales.

Weaknesses:

While flexible and able to deal with non-stationarity, the wavelet approach does not really allow investigation of multiple factors at the same time but is limited to uni- and bivariate analyses. This limits the interpretability of the effect of complex climate patterns while also 'adjusting' for the changing control environment. There is also some concern that the choice of the wavelet and transforms used for different analyses (Morelet, Coiflet, maximal overlap discreet transform) may affect the results. The reasons for choosing these particular wavelets and transforms are not always evident.

The attempt to investigate the effect of longer terms / irregular period climate events is laudable. However, why were the analyses restricted to only ENSO (measured as SST)? Other climate factors such as e.g. the Indian Ocean Dipole (i.e. the difference in SST between the western and eastern Indian Ocean) are also known to affect climate and rainfall patterns in Eastern Africa.

Nevertheless, this work is a compelling demonstration of the utility of wavelets for the analyses of (non-stationary) epidemiological time series data.