Relationship of cholera incidence to El Niño and solar activity elucidated by time-series analysis

Spectral study of COVID-19 pandemic in Japan: The dependence of spectral gradient on the population size of the community

We have carried out spectral analysis of coronavirus disease 2019 (COVID-19) notifications in all 47 prefectures in Japan. The results confirm that the power spectral densities (PSDs) of the data from each prefecture show exponential characteristics, which are universally observed in the PSDs of time series generated by nonlinear dynamical systems, such as the susceptible/exposed/infectious/recovered (SEIR) epidemic model. The exponential gradient increases with the population size. For all prefectures, many spectral lines observed in each PSD can be fully assigned to a fundamental mode and its harmonics and subharmonics, or linear combinations of a few fundamental periods, suggesting that the COVID-19 data are substantially noise-free. For prefectures with large population sizes, PSD patterns obtained from segment time series behave in response to the introduction of public and workplace vaccination programs as predicted by theoretical studies based on the SEIR model. The meaning of the relationship between the exponential gradient and the population size is discussed.

Time series analysis of daily reported number of new positive cases of COVID-19 in Japan from January 2020 to February 2023

This study investigated temporal variations of the COVID-19 pandemic in Japan using a time series analysis incorporating maximum entropy method (MEM) spectral analysis, which produces power spectral densities (PSDs). This method was applied to daily data of COVID-19 cases in Japan from January 2020 to February 2023. The analyses confirmed that the PSDs for data in both the pre- and post-Tokyo Olympics periods show exponential characteristics, which are universally observed in PSDs for time series generated from nonlinear dynamical systems, including the so-called susceptible/exposed/infectious/recovered (SEIR) model, well-established as a mathematical model of temporal variations of infectious disease outbreaks. The magnitude of the gradient of exponential PSD for the pre-Olympics period was smaller than that of the post-Olympics period, because of the relatively high complex variations of the data in the pre-Olympics period caused by a deterministic, nonlinear dynamical system and/or undeterministic noise. A 3-dimensional spectral array obtained by segment time series analysis indicates that temporal changes in the periodic structures of the COVID-19 data are already observable before the commencement of the Tokyo Olympics and immediately after the introduction of mass and workplace vaccination programs. Additionally, the possibility of applying theoretical studies for measles control programs to COVID-19 is discussed.

The changing relationship between Cholera and interannual climate variables in Kolkata over the past century

BACKGROUND

In the Bengal Delta, research has shown that climate and cholera are linked. One demonstration of this is the relationship between interannual ocean-atmospheric oscillations such as the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). What remains unclear in the present literature is the nature of this relationship in the specific context of Kolkata, and how this relationship may have changed over time.

RESULTS

In this study, we analyse the changing relationship between ENSO and IOD with cholera in Kolkata over recent (1999-2019) and historical (1897-1941) time intervals. Wavelet coherence analysis revealed significant non-stationary association at 2-4 year and 4-8 year periods between cholera and both interannual timeseries during both time intervals. However, coherence was notably weakened in the recent interval, particularly with regards to ENSO, a result supported by a complementary SARIMA analysis. Similar coherence patterns with temperature indicate it could be an important mediating factor in the relationship between cholera and oscillating climate phenomena in Kolkata.

CONCLUSIONS

This study reveals a shifting relationship between cholera and climate variables (ENSO and IOD) in Kolkata, suggesting a decoupling between environmental influences and cholera transmission in recent years. Our results therefore do not suggest that an intensification of ENSO is likely to significantly influence cholera in the region. We also find that the relationship between cholera and interannual climate variables is distinct to Kolkata, highlighting the spatial heterogeneity of the climate-cholera relationship even within the Bengal Delta.

Effects of El Niño/La Niña on the Number of Imported Shigellosis Cases in the Republic of Korea, 2004-2017

Shigellosis is a major diarrheal disease in low- and middle-income countries. Although the incidence of such diseases in South and Southeast Asia has been associated with climate fluctuations linked to the El Niño-Southern Oscillation (ENSO), the impact of ENSO on shigellosis infections remains unknown. Data reported to being infected with shigellosis while traveling abroad from 2004 to 2017 were obtained from the Korea Centers for Disease Control and Prevention. We investigated the relationship between the Oceanic Niño Index (ONI) and Indian Ocean Dipole Mode Index and the relative risk of shigellosis in outbound travelers using distributed lag linear and non-linear models. From 2004 to 2017, 87.1% of imported shigellosis was infected in South and Southeast Asian countries. The relative risk of imported shigellosis infection in outbound travelers increased as the ONI decreased. In the association with the five-month cumulative ONI, the relative risk of infection continuously increased as the La Niña index gained strength. Climate fluctuations associated with the La Niña phenomenon in South and Southeast Asian countries can lead to issues in sanitation and water safety. Our findings suggest that the decreasing trend in the ONI is associated with an increased incidence of shigellosis in these countries.

How climate change can affect cholera incidence and prevalence? A systematic review

Although the number of cholera infection decreased universally, climate change can potentially affect both incidence and prevalence rates of disease in endemic regions. There is considerable consistent evidence, explaining the associations between cholera and climatic variables. However, it is essentially required to compare and interpret these relationships globally. The aim of the present study was to carry out a systematic review in order to identify and appraise the literature concerning the relationship between nonanthropogenic climatic variabilities such as extreme weather- and ocean-related variables and cholera infection rates. The systematic literature review of studies was conducted by using determined search terms via four major electronic databases (PubMed, Web of Science, Embase, and Scopus) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. This search focused on published articles in English-language up to December 31, 2018. A total of 43 full-text studies that met our criteria have been identified and included in our analysis. The reviewed studies demonstrated that cholera incidence is highly attributed to climatic variables, especially rainfall, temperature, sea surface temperature (SST) and El Niño Southern Oscillation (ENSO). The association between cholera incidence and climatic variables has been investigated by a variety of data analysis methodologies, most commonly time series analysis, generalized linear model (GLM), regression analysis, and spatial/GIS. The results of this study assist the policy-makers who provide the efforts for planning and prevention actions in the face of changing global climatic variables.

Influences of heatwave, rainfall, and tree cover on cholera in Bangladesh

Cholera is a severe diarrheal disease and remains a global threat to public health. Climate change and variability have the potential to increase the distribution and magnitude of cholera outbreaks. However, the effect of heatwave on the occurrence of cholera at individual level is still unclear. It is also unknown whether the local vegetation could potentially mitigate the effects of extreme heat on cholera outbreaks. In this study, we designed a case-crossover study to examine the association between the risk of cholera and heatwaves as well as the modification effects of rainfall and tree cover. The study was conducted in Matlab, a cholera endemic area of rural Bangladesh, where cholera case data were collected between January 1983 and April 2009. The association between the risk of cholera and heatwaves was examined using conditional logistic regression models. The results showed that there was a higher risk of cholera two days after heatwaves (OR = 1.53, 95% CI: 1.07-2.19) during wet days (rainfall > 0 mm). For households with less medium-dense tree cover, the heatwave after a 2-day lag was positively associated (OR = 1.80, 95% CI: 1.01-3.22) with the risk of cholera during wet days. However, for households with more medium-dense tree cover, the association between the risk of cholera and heatwave in 2-day lag was not significant. These findings suggest that heatwaves might promote the occurrence of cholera, while this relationship was modified by rainfall and tree cover. Further investigations are needed to explore major mechanisms underlying the association between heatwaves and cholera as well as the beneficial effects of tree cover.

El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers

The El Niño Southern Oscillation (ENSO) is an important mode of climatic variability that exerts a discernible impact on ecosystems and society through alterations in climate patterns. For this reason, ENSO has attracted much interest in the climate and health science community, with many analysts investigating ENSO health links through considering the degree of dependency of the incidence of a range of climate diseases on the occurrence of El Niño events. Because of the mounting interest in the relationship between ENSO as a major mode of climatic variability and health, this paper presents an overview of the basic characteristics of the ENSO phenomenon and its climate impacts, discusses the use of ENSO indices in climate and health research, and outlines the present understanding of ENSO health associations. Also touched upon are ENSO-based seasonal health forecasting and the possible impacts of climate change on ENSO and the implications this holds for future assessments of ENSO health associations. The review concludes that there is still some way to go before a thorough understanding of the association between ENSO and health is achieved, with a need to move beyond analyses undertaken through a purely statistical lens, with due acknowledgement that ENSO is a complex non-canonical phenomenon, and that simple ENSO health associations should not be expected.

Collaborative Research with Chinese, Indian, Filipino and North European Research Organizations on Infectious Disease Epidemics

In this report, we present a short review of applications of time series analysis, which consists of spectral analysis based on the maximum entropy method in the frequency domain and the least squares method in the time domain, to the incidence data of infectious diseases. This report consists of three parts. First, we present our results obtained by collaborative research on infectious disease epidemics with Chinese, Indian, Filipino and North European research organizations. Second, we present the results obtained with the Japanese infectious disease surveillance data and the time series numerically generated from a mathematical model, called the susceptible/exposed/infectious/recovered (SEIR) model. Third, we present an application of the time series analysis to pathologic tissues to examine the usefulness of time series analysis for investigating the spatial pattern of pathologic tissue. It is anticipated that time series analysis will become a useful tool for investigating not only infectious disease surveillance data but also immunological and genetic tests.

Time series analysis of reported cases of hand, foot, and mouth disease from 2010 to 2013 in Wuhan, China

Background

Hand, foot, and mouth disease (HFMD) is an infectious disease caused by a group of enteroviruses, including Coxsackievirus A16 (CVA16) and Enterovirus A71 (EV-A71). In recent decades, Asian countries have experienced frequent and widespread HFMD outbreaks, with deaths predominantly among children. In several Asian countries, epidemics usually peak in the late spring/early summer, with a second small peak in late autumn/early winter. We investigated the possible underlying association between the seasonality of HFMD epidemics and meteorological variables, which could improve our ability to predict HFMD epidemics.

Methods

We used a time series analysis composed of a spectral analysis based on the maximum entropy method (MEM) in the frequency domain and the nonlinear least squares method in the time domain. The time series analysis was applied to three kinds of monthly time series data collected in Wuhan, China, where high-quality surveillance data for HFMD have been collected: (i) reported cases of HFMD, (ii) reported cases of EV-A71 and CVA16 detected in HFMD patients, and (iii) meteorological variables.

Results

In the power spectral densities for HFMD and EV-A71, the dominant spectral lines were observed at frequency positions corresponding to 1-year and 6-month cycles. The optimum least squares fitting (LSF) curves calculated for the 1-year and 6-month cycles reproduced the bimodal cycles that were clearly observed in the HFMD and EV-A71 data. The peak months on the LSF curves for the HFMD data were consistent with those for the EV-A71 data. The risk of infection was relatively high at 10 °C ≤ t < 15 °C (t, temperature [°C]) and 15 °C ≤ t < 20 °C, and peaked at 20 °C ≤ t < 25 °C.

Conclusion

In this study, the HFMD infections occurring in Wuhan showed two seasonal peaks, in summer (June) and winter (November or December). The results obtained with a time series analysis suggest that the bimodal seasonal peaks in HFMD epidemics are attributable to EV-A71 epidemics. Our results suggest that controlling the spread of EV-A71 infections when the temperature is approximately 20–25 °C should be considered to prevent HFMD infections in Wuhan, China.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-015-1233-0) contains supplementary material, which is available to authorized users.

Global climate anomalies and potential infectious disease risks: 2014-2015

Background: The El Niño/Southern Oscillation (ENSO) is a global climate phenomenon that impacts human infectious disease risk worldwide through droughts, floods, and other climate extremes. Throughout summer and fall 2014 and winter 2015, El Niño Watch, issued by the US National Oceanic and Atmospheric Administration, assessed likely El Niño development during the Northern Hemisphere fall and winter, persisting into spring 2015. Methods: We identified geographic regions where environmental conditions may increase infectious disease transmission if the predicted El Niño occurs using El Niño indicators (Sea Surface Temperature [SST], Outgoing Longwave Radiation [OLR], and rainfall anomalies) and literature review of El Niño-infectious disease associations. Results: SSTs in the equatorial Pacific and western Indian Oceans were anomalously elevated during August-October 2014, consistent with a developing weak El Niño event. Teleconnections with local climate is evident in global precipitation patterns, with positive OLR anomalies (drier than average conditions) across Indonesia and coastal southeast Asia, and negative anomalies across northern China, the western Indian Ocean, central Asia, north-central and northeast Africa, Mexico/Central America, the southwestern United States, and the northeastern and southwestern tropical Pacific. Persistence of these conditions could produce environmental settings conducive to increased transmission of cholera, dengue, malaria, Rift Valley fever, and other infectious diseases in regional hotspots as during previous El Niño events. Discussion and Conclusions: The current development of weak El Niño conditions may have significant potential implications for global public health in winter 2014-spring 2015. Enhanced surveillance and other preparedness measures in predicted infectious disease hotspots could mitigate health impacts.

Aneurysmal subarachnoid hemorrhage: relationship to solar activity in the United States, 1988-2010

Aneurysmal subarachnoid hemorrhage (SAH) is a common condition treated by neurosurgeons. The inherent variability in the incidence and presentation of ruptured cerebral aneurysms has been investigated in association with seasonality, circadian rhythm, lunar cycle, and climate factors. We aimed to identify an association between solar activity (solar flux and sunspots) and the incidence of aneurysmal SAH, all of which appear to behave in periodic fashions over long time periods. The Nationwide Inpatient Sample (NIS) provided longitudinal, retrospective data on patients hospitalized with SAH in the United States, from 1988 to 2010, who underwent aneurysmal clipping or coiling. Solar activity and SAH incidence data were modeled with the cosinor methodology and a 10-year periodic cycle length. The NIS database contained 32,281 matching hospitalizations from 1988 to 2010. The acrophase (time point in the cycle of highest amplitude) for solar flux and for sunspots were coincident. The acrophase for aneurysmal SAH incidence was out of phase with solar activity determined by non-overlapping 95% confidence intervals (CIs). Aneurysmal SAH incidence peaks appear to be delayed behind solar activity peaks by 64 months (95% CI; 56-73 months) when using a modeled 10-year periodic cycle. Solar activity (solar flux and sunspots) appears to be associated with the incidence of aneurysmal SAH. As solar activity reaches a relative maximum, the incidence of aneurysmal SAH reaches a relative minimum. These observations may help identify future trends in aneurysmal SAH on a population basis.

Influence of climate factors on Vibrio cholerae dynamics in the Pearl River estuary, South China

Current research has seldom focused on the quantitative relationships between Vibrio cholerae (V. cholerae) and climate factors owing to the complexities and high cost of field observation in the aquatic environment. This study has focused on the relationships between V. cholerae and climate factors based on linear regression method and data partition method. Data gathered from 2008 to 2009 in the Pearl River estuary, South China, were adopted. Positive rate of V. cholerae was correlated closely with monthly climate factors of water temperature and air temperature, respectively in 2009. Quarterly data analysis from 2008 to 2009 showed that there existed seasonal characteristic for V. cholerae. Positive rate of V. cholerae was correlated positively with quarterly climate factors of land surface temperature, pH, water temperature, air temperature and rainfall, respectively and negatively with quarterly air pressure. Partition data analysis in 2009 showed that there existed geography region characteristic for V. cholerae. V. cholerae dynamics was closely correlated to climate factors in the downstream area. However, it was more greatly affected by human geography factors in the urban area. Positive annual rate of V. cholerae was higher in the downstream area than in the urban area both in 2008 and 2009. At last, a cellular automaton model was used to simulate V. cholerae diffusion downstream, and the distribution of V. cholerae obtained from this model was similar to that obtained from the field observations.

Effect of temperature, relative humidity and rainfall on rotavirus infections in Kolkata, India

Rotavirus is a common viral cause of severe diarrhoea. For the underlying cause of rotavirus seasonality, the meteorological factor has been suspected, whereas quantitative correlation between seasonality and meteorological factor has not been fully investigated. In this study, we investigated the correlation of temporal patterns of the isolation rate of rotavirus with meteorological condition (temperature, relative humidity, rainfall) in Kolkata, India. We used time-series analysis combined with spectral analysis and least squares method. A 1-year cycle explained underlying variations of rotavirus and meteorological data. The 1-year cycle for rotavirus data was correlated with an opposite phase to that for meteorological data. Relatively high temperature could be associated with a low value of isolation rate of rotavirus in the monsoon season. Quantifying a correlation of rotavirus infections with meteorological conditions might prove useful in predicting rotavirus epidemics and health services could plan accordingly.

The effectiveness of public health interventions to reduce the health impact of climate change: a systematic review of systematic reviews

BACKGROUND

Climate change is likely to be one of the most important threats to public health in the coming years. Yet despite the large number of papers considering the health impact of climate change, few have considered what public health interventions may be of most value in reducing the disease burden. We aimed to evaluate the effectiveness of public health interventions to reduce the disease burden of high priority climate sensitive diseases.

METHODS AND FINDINGS

For each disease, we performed a systematic search with no restriction on date or language of publication on Medline, Web of Knowledge, Cochrane CENTRAL and SCOPUS up to December 2010 to identify systematic reviews of public health interventions. We retrieved some 3176 records of which 85 full papers were assessed and 33 included in the review. The included papers investigated the effect of public health interventions on various outcome measures. All interventions were GRADE assessed to determine the strength of evidence. In addition we developed a systematic review quality score. The interventions included environmental interventions to control vectors, chemoprophylaxis, immunization, household and community water treatment, greening cities and community advice. For most reviews, GRADE showed low quality of evidence because of poor study design and high heterogeneity. Also for some key areas such as floods, droughts and other weather extremes, there are no adequate systematic reviews of potential public health interventions.

CONCLUSION

In conclusion, we found the evidence base to be mostly weak for environmental interventions that could have the most value in a warmer world. Nevertheless, such interventions should not be dismissed. Future research on public health interventions for climate change adaptation needs to be concerned about quality in study design and should address the gap for floods, droughts and other extreme weather events that pose a risk to health.

Cyclic patterns of cerebral malaria admissions in Papua New Guinea for the years 1987-1996

Data on the dynamics of malaria incidence, admissions and mortality and their best possible description are very important to better forecast and assess the implementation of programmes to register, monitor (e.g. by remote sensing) and control the disease, especially in endemic zones. Semi-annual and seasonal cycles in malaria rates have been observed in various countries and close similarity with cycles in the natural environment (temperature, heliogeophysical activity, etc.), host immunity and/or virulence of the parasite suggested. This study aimed at confirming previous results on malaria cyclicity by exploring whether trans-year and/or multiannual cycles might exist. The exploration of underlying chronomes (time structures) was done with raw data (without smoothing) by linear and nonlinear parametric regression models, autocorrelation, spectral (Fourier) and periodogram regression analysis. The strongest cyclical patterns of detrended malaria admissions were (i) annual period of 1·0 year (12 months or seasonality); (ii) quasi-biennial cycle of about 2·25 years; and (iii) infrannual, circadecennial cycle of about 10·3 years. The seasonal maximum occurred in May with the minimum in September. Notably, these cycles corresponded to similar cyclic components of heliogeophysical activity such as sunspot seasonality and solar activity cyclicities and well-known climate/weather oscillations. Further analyses are thus warranted to investigate such similarities. In conclusion, multicomponent cyclical dynamics of cerebral malaria admissions in Papua New Guinea were observed thus allowing more specific analyses and modelling as well as correlations with environmental factors of similar cyclicity to be explored. Such further results might also contribute to and provide more precise estimates for the forecasting and prevention, as well as the better understanding, of the dynamics and aetiology of this vector-borne disease.

Time-series analysis of hepatitis A, B, C and E infections in a large Chinese city: application to prediction analysis

Viral hepatitis is recognized as one of the most frequently reported diseases, and especially in China, acute and chronic liver disease due to viral hepatitis has been a major public health problem. The present study aimed to analyse and predict surveillance data of infections of hepatitis A, B, C and E in Wuhan, China, by the method of time-series analysis (MemCalc, Suwa-Trast, Japan). On the basis of spectral analysis, fundamental modes explaining the underlying variation of the data for the years 2004-2008 were assigned. The model was calculated using the fundamental modes and the underlying variation of the data reproduced well. An extension of the model to the year 2009 could predict the data quantitatively. Our study suggests that the present method will allow us to model the temporal pattern of epidemics of viral hepatitis much more effectively than using the artificial neural network, which has been used previously.

Dynamics of cholera outbreaks in Great Lakes region of Africa, 1978-2008

Cholera outbreaks have occurred in Burundi, Rwanda, Democratic Republic of Congo, Tanzania, Uganda, and Kenya almost every year since 1977-1978, when the disease emerged in these countries. We used a multiscale, geographic information system-based approach to assess the link between cholera outbreaks, climate, and environmental variables. We performed time-series analyses and field investigations in the main affected areas. Results showed that cholera greatly increased during El Nino warm events (abnormally warm El Ninos) but decreased or remained stable between these events. Most epidemics occurred in a few hotspots in lakeside areas, where the weekly incidence of cholera varied by season, rainfall, fluctuations of plankton, and fishing activities. During lull periods, persistence of cholera was explained by outbreak dynamics, which suggested a metapopulation pattern, and by endemic foci around the lakes. These links between cholera outbreaks, climate, and lake environments need additional, multidisciplinary study.

MEM spectral analysis for predicting influenza epidemics in Japan

OBJECTIVES

The prediction of influenza epidemics has long been the focus of attention in epidemiology and mathematical biology. In this study, we tested whether time series analysis was useful for predicting the incidence of influenza in Japan.

METHODS

The method of time series analysis we used consists of spectral analysis based on the maximum entropy method (MEM) in the frequency domain and the nonlinear least squares method in the time domain. Using this time series analysis, we analyzed the incidence data of influenza in Japan from January 1948 to December 1998; these data are unique in that they covered the periods of pandemics in Japan in 1957, 1968, and 1977.

RESULTS

On the basis of the MEM spectral analysis, we identified the periodic modes explaining the underlying variations of the incidence data. The optimum least squares fitting (LSF) curve calculated with the periodic modes reproduced the underlying variation of the incidence data. An extension of the LSF curve could be used to predict the incidence of influenza quantitatively.

CONCLUSIONS

Our study suggested that MEM spectral analysis would allow us to model temporal variations of influenza epidemics with multiple periodic modes much more effectively than by using the method of conventional time series analysis, which has been used previously to investigate the behavior of temporal variations in influenza data.

Time series analysis of incidence data of influenza in Japan

Background

Much effort has been expended on interpreting the mechanism of influenza epidemics, so as to better predict them. In addition to the obvious annual cycle of influenza epidemics, longer-term incidence patterns are present. These so-called interepidemic periods have long been a focus of epidemiology. However, there has been less investigation of the interepidemic period of influenza epidemics. In the present study, we used spectral analysis of influenza morbidity records to indentify the interepidemic period of influenza epidemics in Japan.

Methods

We used time series data of the monthly incidence of influenza in Japan from January 1948 through December 1998. To evaluate the incidence data, we conducted maximum entropy method (MEM) spectral analysis, which is useful in investigating the periodicities of shorter time series, such as that of the incidence data used in the present study. We also conducted a segment time series analysis and obtained a 3-dimensional spectral array.

Results

Based on the results of power spectral density (PSD) obtained from MEM spectral analysis, we identified 3 periodic modes as the interepidemic periods of the incidence data. Segment time series analysis revealed that the amount of amplitude of the interepidemic periods increased during the occurrence of influenza pandemics and decreased when vaccine programs were introduced.

Conclusions

The findings suggest that the temporal behavior of the interepidemic periods of influenza epidemics is correlated with the magnitude of cross-reactive immune responses.