Climate change and the emergence of bat-borne viruses

Many zoonotic diseases are sensitive to climate change as a result of habitat shifts leading to contact between humans and new species, as well as changes in temperature and rainfall patterns. Recently Virus In the study, the researchers discussed correlations between the emergence and abundance of bat-borne diseases and climate change and events.

Learn: Climate Anomalies and Spills of Bat-borne Viral Diseases in the Asia-Pacific Region and the Arabian Peninsula. Image Credit: by-studio / Shutterstock.com

About study

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

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

Most viruses appear in one geographic area, with the exception of NiV, which initially appeared in Malaysia in 1998, followed by India in 2001, and the Philippines in 2014. Both NiV and HeV regularly spread to Bangladesh and Australia after their initial emergence.

Five occurrence events occurred during the cooling La Niña event, while four events occurred during the warming phase of El Nio. The other three occurrence events occur during the neutral phase.

(A) Map showing the location of emergence of bat-borne viruses in the Asia-Pacific and Arabian Peninsula (see Table 1) and the bat reservoirs of each virus.  Virus names are colored according to the ENSO phase at which they appear: neutral phase (black), cold La Niña phase (blue), or warm El Niño phase (red).  (B) Variation of the NINO 3.4 index characterizing the El Nio Southern Oscillation (ENSO) taken from the National Oceanic and Atmospheric Administration (NOAA, https://www.noaa.gov, accessed 17 May 2022) from 1990 to 2020. Outline red and blue thresholds indicate El Nio warming or cooling La Nia climate anomalies, respectively.  The arrows indicate the time of emergence of new bat-borne viruses in the Asia-Pacific region and the Arabian Peninsula (see Table 1).  Virus names are colored according to the ENSO phase at which they appear: neutral phase (black), cold La Niña phase (blue), or warm El Niño phase (red).

(A) Map showing the location of occurrence of bat-borne viruses in the Asia-Pacific region and the Arabian Peninsula (see Table 1) and the bat reservoirs of each virus. Virus names are colored according to the ENSO phase at which they appear: neutral phase (black), cold La Niña phase (blue), or warm El Niño phase (red). (B) Variation of the NINO 3.4 index characterizing the El Nio Southern Oscillation (ENSO) taken from the National Oceanic and Atmospheric Administration (NOAA, https://www.noaa.gov, accessed 17 May 2022) from 1990 to 2020. Outline red and blue thresholds indicate El Nio warming or cooling La Nia climate anomalies, respectively. The arrows indicate the time of emergence of new bat-borne viruses in the Asia-Pacific region and the Arabian Peninsula (see Table 1). Virus names are colored according to the ENSO phase at which they appear: neutral phase (black), cold La Niña phase (blue), or warm El Niño phase (red).

Study findings

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

The cross-correlation analysis of these two spillover events shows a significant correlation with climate variables. More specifically, NiV spillover events correlated with monthly rainfall, temperature, and ground surface temperature anomalies, at one-month, one-month, and ten-month intervals, respectively. No significant correlation was found between NiV spillover events and El Nio events.

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

Two logistic regression models were built based on the lag values ​​obtained by cross-correlation analysis. The HeV repeat event model in Australia maintains rainfall as a variable but has no significant effect.

Temperature, ground surface temperature anomalies, and El Nio index values ​​are also included. Rainfall has a significant effect in the model for NiV overflow events, while the anomaly of ground surface temperature, mean temperature, and El Niño index do not.

Structural equation modeling was used to confirm the results for HeV. This analysis shows a significant correlation between repeated spills and monthly mean temperatures, ground surface temperature anomalies, and El Nio values.

The same variables were used for NiV, although some were not significant. Moreover, this analysis reveals that NiV abundance in Bangladesh only correlates with monthly mean temperature changes, whereas rainfall has no effect. Notably, these findings contrast with those reported in previous models.

Event coincidence analysis was then used to test the hypothesis that outbreaks of bat-borne viral diseases were preceded by El Nio/La Niña climatic events. For this purpose, random associations between the emergence of this disease and climatic events are reported. Notably, a non-random association was observed with significant value precursor coincidence rates when Australian bat lyssavirus and NiV were excluded from analysis.

The same analysis was also used to explore the possible association between El Nio values ​​and the incidence of NiV outbreaks, again demonstrating a random statistical relationship. A non-random significant relationship was only observed if a three-month gap was used, thus indicating a global break from climate events.

The association between this climatic event and the HeV outbreak in Australia is significant, as indicated by the seven-month lag period estimated in the cross-correlation time series analysis.

Time series and residual autocorrelation function (ACF) with significant autocorrelation values ​​on the dashed line (left column) and wavelet power spectrum (right column) from January 1993 to June 2020 (330 months) of (A) monthly temperatures in Bangladesh, ( B) monthly precipitation in Bangladesh, (C) monthly temperature in Australia, (D) monthly precipitation in Australia, and (E) NINO 3.4 index value decomposed in subtle trends and seasonal effects.  The wavelet power value increases from blue to red, and the black contour line indicates a 5% significance level.

Time series and residual autocorrelation function (ACF) with significant autocorrelation values ​​on the dashed line (left column) and wavelet power spectrum (right column) from January 1993 to June 2020 (330 months) of (A) monthly temperatures in Bangladesh, ( B) monthly precipitation in Bangladesh, (C) monthly temperature in Australia, (D) monthly precipitation in Australia, and (E) NINO 3.4 index value decomposed in subtle trends and seasonal effects. The wavelet power value increases from blue to red, and the black contour line indicates a 5% significance level.

Conclusion

Current studies have revealed that the abundance patterns of several bat-transmitted viruses are strongly influenced by climate variability, each of which is associated with a different time lag. In particular, NiV spillover events are likely to be altered by unknown factors; however, this event was significantly influenced by winter temperatures, along with a potential correlation with rainfall.

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

As climate modeling shows, El Nio events will increase in frequency and intensity in the future. Therefore, further investigation is urgently needed to determine the risk that this event will lead to the emergence of more diseases.

Journal reference:

  • Latinne, A. &, Morand, S. (2022). Climate Anomalies and Spills of Bat-borne Viral Diseases in the Asia-Pacific Region and the Arabian Peninsula. Virus 14(5). doi:10.3390/v14051100.

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