Climate change and the emergence of bat-borne viruses

Many zoonotic diseases are sensitive to changes in climate as a result of shifting habitats that introduce contact between humans and new species, as well as alterations in temperature and precipitation patterns.

In a recent Viruses study, researchers discuss correlations between the emergence and spillover of bat-borne diseases and climate events and changes.

About the study

Ten bat-borne viruses that emerged in either the Asia-Pacific region or Arabian Peninsula between 1990 and 2020 were investigated in the current study. These viruses included four belonging to the Coronaviridae family, as well as three Paramyxoviridae, two Reoviridae, and one Rhabdoviridae viruses.

Nine of these viruses were detected in humans, whereas the swine acute diarrhea syndrome coronavirus (SADS-CoV) was found in swine populations. Livestock were involved as intermediate hosts in the emergence of both Hendra virus (HeV) and Nipah virus (NiV).

Most viruses emerged in a single geographic area, except for NiV, which originally emerged in Malaysia in 1998, followed by India in 2001, and the Philippines in 2014. Both NiV and HeV regularly spilled into Bangladesh and Australia following their initial emergence.

Five of the emergence events occurred during the La Niña cooling event, while four occurred during the warming El Niño phase. The other three emergence events occurred during neutral phases.

Study findings

Residual autocorrelation function (ACF) and wavelet analyses revealed strong seasonal patterns of temperature and rainfall in Bangladesh and Australia during the emergence of both NiV and HeV.

Cross-correlation analysis of these two spillover events showed that there were significant correlations with climate variables. More specifically, NiV spillover events correlated with monthly rainfall, temperature, and land surface temperature anomalies, with intervals of one month, one month, and ten months, respectively. No significant correlation was found between spillover events of NiV and El Niño events.

Comparatively, HeV showed correlations with El Niño index values, monthly rainfall and temperature, as well as land surface temperature anomalies, with intervals of seven months, one month, zero months, and three months, respectively.

Two logistic regression models were constructed based on the lag values obtained by the cross-correlation analysis. The model of recurring spillover events of HeV in Australia retained rainfall as a variable but had no significant effects.

Temperature, land surface temperature anomalies, and El Niño index values were also included. Rainfall had a significant effect in the model for spillover events of NiV, whereas land surface temperature anomalies, mean temperature, and the El Niño index did not.

Structural equation modeling was used to confirm the results for HeV. This analysis showed significant correlations between recurring spillovers and mean monthly temperatures, anomalies in land surface temperature, and El Niño values.

The same variables were used for NiV, although some were non-significant. Moreover, this analysis revealed that spillovers of NiV in Bangladesh only correlated with mean monthly temperature changes, whereas rainfall had no effect. Notably, these findings contrasted those reported in the previous model.

Event coincidence analysis was then used to test the hypothesis that outbreaks of bat-borne viral diseases were preceded by El Niño/La Niña climate events. To this end, a random association between the emergence of these diseases and climate events was reported. Notably, a non-random association was observed with a significant value precursor coincidence rate when the Australian bat lyssavirus and NiV were excluded from the analysis.

The same analysis was also used to explore possible associations between El Niño values and NiV outbreak events, once again showing a random statistical relationship. Non-random significant relationships were only observed if a lag of three months was used, thus suggesting a global lag of the climate events.

The relationship between these climate events and HeV outbreaks in Australia was significant, as demonstrated by the seven-month lag period estimated in the cross-correlation time-series analysis.

Conclusions

The current study has revealed that the spillover patterns of several bat-borne viruses are strongly impacted by climate variability, each of which is associated with different time lags. In particular, NiV spillover events are likely altered by unknown factors; however, these events are significantly affected by winter temperature, along with a potential correlation with rainfall.

While no relationship was observed between El Niño or La Niña events and NiV spillover, the same was not true for the spillover of HeV in Australia. In fact, many HeV outbreaks were preceded by these climate events. Significant correlations were also found between other sources of climate variability and HeV spillover, including temperature and land surface temperature anomalies.

As climate modeling suggests, El Niño events will increase in frequency and intensity in the future. Therefore, further investigation is urgently needed to determine the risk that these events will pose for the emergence of more diseases.