A Process for Detecting Infectious Diseases that are Sensitive to Climate Change

Martin King^*
*Correspondence: Martin King, Department of Vector Biology and Control, Rajendra Memorial Research Institute of Medical Sciences, Patna, India, Email:

Introduction

Climate change poses a serious and growing threat to human health, particularly in the case of infectious diseases. It is still difficult to systematically and quantitatively screen for infectious diseases that are sensitive to climate change due to the complex interactions between climate variables and infectious disease components (pathogen, host and transmission environment). Relative Sensitivity, a brand-new statistical indicator, is what we propose as a solution to this problem. It measures the difference in the infectious diseases sensitivity to climate variables for two distinct climate statuses historical climate and current climate in the exposure and non-exposure groups. This Relative Sensitivity indicators usefulness has been demonstrated by the case study in China's Anhui Province.

Description

The application results show critical responsiveness of numerous pandemic irresistible sicknesses to environmental change through changing climatic factors, like temperature, precipitation and outright mugginess. This study provides novel evidence that absolute humidity has a significant impact on a number of infectious diseases that have been observed in Anhui Province. These diseases include dysentery, hand, foot and mouth disease, hepatitis A, haemorrhagic fever, typhoid fever, malaria, meningitis, influenza, schistosomiasis and meningitis. In addition, certain infectious diseases are more vulnerable to climate change in rural areas than in urban ones. Future health inputs that take into account climate changes spatial variability can learn from this insight.

In its fifth report, the Intergovernmental Panel on Climate Change (IPCC) says that the Earth system is clearly warming and will probably get dangerously warmer this century. Climate change has a significant impact on human health as well as the physical Earth system, particularly in the case of infectious diseases. The majority of infectious diseases have three essential components: a transmission environment, a host (or pathogen) and an agent. Climate change may have an impact on the entire process of a diseases development for the majority of climate-sensitive infectious diseases. This includes the availability and means of these pathogens transmission environments, as well as the survival, reproduction, or distribution of disease pathogens and their hosts. The spread of malaria, for instance, is impacted by rising temperatures; whereas the impact of climate change on infectious diseases has received significant attention.

However, previous research has primarily focused on determining how climate change affects one aspect of the spread of infectious diseases. Due to the intricate interactions between climate variables and the three primary components of infectious disease development, it is still unclear how susceptible infectious diseases are to climate change. Without sufficient research, it will continue to be difficult to conduct systematic quantitative screenings for climatesensitive infectious diseases [1]. Correlation or regression analysis is used the most frequently in the vast body of research on climate-sensitive diseases. For instance, the crude relationship between the logarithm of diarrhoea morbidity and average weather variables can be described using Pearson's correlation coefficients. A climate variation-guided Poisson regression model was used in a subsequent study to predict the dynamics of diarrhoea-associated morbidity in subtropical Taiwan and measure the connection between climate change and diarrhoea-associated morbidity.

However, such correlation analysis can only determine the static relationship between infectious disease-related parameters (morbidity) and climate variables. Cross-correlation analysis and other time series methods, on the other hand, offer opportunities to identify time lags and the temporal correlation between incidence data and climate data. Climate change can have nonlinear effects on biological systems, but they also assume that diseases respond linearly to climate variables [2]. This demonstrates that the effects on irresistible illnesses may be different under various environment systems. Therefore, nonlinear relationships would not be detected by methods that assume linear relationships. In contrast, Woolf's relative risk statistic (RR), which has been used to estimate the increased risk of contracting a disease prior to and following the occurrence of a particular condition (or trait), has become a common measurement in biomedical research. Later, numerous additional statistical factors were proposed, such as the haplotype relative risk (HRR) method, which is used to determine the disease risk in the presence of specific antigens or phenotypes.

The WHO also developed the potential impact fraction (PIF) to estimate the disease burden brought on by a change in a particular risk factor using relative risk measurement. A metric that is comparable to the RR and is used to compare the risk variation corresponding to a specified change in the level of a risk factor can be used to reasonably state that an infectious disease is as sensitive to climate change with the RR as a reference if the relationship between infectious disease-related parameters and climate variables varies at two different climate statuses (i.e., historical climate and present climate) [3].

Consequently, this study proposes a comprehensive and statistically significant approach to determining which infectious diseases are sensitive to climate change. For the purpose of evaluating strategies to mitigate the effects of climate change on human health, a method like this is especially important.

This is especially true when governments decide on appropriate investments in health care in light of climate change. Although infectious diseases have been affected by climate change worldwide, the nature and severity of those effects vary greatly. The impacts of various infectious diseases are distinct and sometimes contentious at various geographic scales due to the intricate interactions between climate variables and the components of the diseases (pathogen, host and transmission environment). In addition, the location of the countries in question, their socioeconomic conditions, pollution and healthcare conditions all play a role in the prevalence of infectious diseases. Infectious diseases relative sensitivity to climate change in Anhui province differs spatially, as we discovered in this study.

Dysentery and HFM, for instance, are more susceptible to climate change in rural areas than in urban ones. Take, for instance, HFM, which is a disease that affects children and is brought on by human enteroviruses, specifically the coxsackie viruses and human enterovirus 71 (HEV71). The resistant serotype or the local sensitivity of temperature and humidity determine the various thermal effects on HEV71. The transmission of HFM and the viability of HEV71 were both enhanced by a warmer and moister environment. In the same way, a lower temperature could slow down gathering and prevent cross-infection between children, stopping the spread of the disease [4,5].

Conclusion

A number of researchers measured the correlation between climate variables and HFM and found that disease incidence increased by 1.4% to 36% for every one degree Celsius increase in temperature and by 0.5% to 4.7% for every one percent increase in relative humidity. On the other hand, the spread of HFM could be affected by non-climatic factors like the density of the child population, good hygiene practices and disease surveillance systems. Importantly, these nonclimatic factors clearly differed in Anhui province. For instance, urban areas spent significantly more on food and health care than rural areas did. Understanding why some diseases are more susceptible to particular climate variables than others is crucial. However, a deeper comprehension of the infectious disease transmission mechanism and additional information regarding the host and pathogen are required for this. It goes beyond the scope of this study and ought to be the subject of subsequent research.

References

1. Hansen, Kristian S., Eleanor Grieve, Amy Mikhail and Ismail Mayan, et al. "Cost-effectiveness of malaria diagnosis using rapid diagnostic tests compared to microscopy or clinical symptoms alone in Afghanistan."Malar J (2015): 1-15.

Google Scholar, Crossref, Indexed at

2. Tahar, Rachida, Collins Sayang, Vincent Ngane Foumane and Georges Soula, et al. "Field evaluation of rapid diagnostic tests for malaria in Yaounde, Cameroon."Acta Trop 125 (2013): 214-219.

Google Scholar, Crossref, Indexed at

3. Jun-Mo, Kang., Cho, P.Y., Moe, M. and Lee, J., et al. “Comparison of the diagnostic performance of microscopic examination with nested polymerase reaction for optimum malaria diagnosis in Upper Myanmar.” Malar J 16(1): 1-8.

Google Scholar, Crossref, Indexed at

4. Falade, Catherine O., IkeOluwapo O. Ajayi, Jesca Nsungwa-Sabiiti and Mohamadou Siribié, et al. "Malaria rapid diagnostic tests and malaria microscopy for guiding malaria treatment of uncomplicated fevers in Nigeria and prereferral cases in 3 African countries."Clin Infect Dis 63 (2016): S290-S297.

Google Scholar, Crossref, Indexed at

5. Baker, Joanne, James McCarthy, Michelle Gatton and Dennis E. Kyle, et al. "Genetic diversity of Plasmodium falciparum histidine-rich protein 2 (PfHRP2) and its effect on the performance of PfHRP2-based rapid diagnostic tests."J Infect Dis 192 (2005): 870-877.

Google Scholar, Crossref, Indexed at