Day-to-Day Population Movement and the Management of Dengue Epidemics

Diseases spectrum in the field of spatiotemporal patterns mining of infectious diseases epidemics: A bibliometric and content analysis

Numerous investigations of the spatiotemporal patterns of infectious disease epidemics, their potential influences, and their driving mechanisms have greatly contributed to effective interventions in the recent years of increasing pandemic situations. However, systematic reviews of the spatiotemporal patterns of communicable diseases are rare. Using bibliometric analysis, combined with content analysis, this study aimed to summarize the number of publications and trends, the spectrum of infectious diseases, major research directions and data-methodological-theoretical characteristics, and academic communities in this field. Based on 851 relevant publications from the Web of Science core database, from January 1991 to September 2021, the study found that the increasing number of publications and the changes in the disease spectrum have been accompanied by serious outbreaks and pandemics over the past 30 years. Owing to the current pandemic of new, infectious diseases (e.g., COVID-19) and the ravages of old infectious diseases (e.g., dengue and influenza), illustrated by the disease spectrum, the number of publications in this field would continue to rise. Three logically rigorous research directions-the detection of spatiotemporal patterns, identification of potential influencing factors, and risk prediction and simulation-support the research paradigm framework in this field. The role of human mobility in the transmission of insect-borne infectious diseases (e.g., dengue) and scale effects must be extensively studied in the future. Developed countries, such as the USA and England, have stronger leadership in the field. Therefore, much more effort must be made by developing countries, such as China, to improve their contribution and role in international academic collaborations.

Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review

Dengue fever is a serious and growing public health problem in Latin America and elsewhere, intensified by climate change and human mobility. This paper reviews the approaches to the epidemiological prediction of dengue fever using the One Health perspective, including an analysis of how Machine Learning techniques have been applied to it and focuses on the risk factors for dengue in Latin America to put the broader environmental considerations into a detailed understanding of the small-scale processes as they affect disease incidence. Determining that many factors can act as predictors for dengue outbreaks, a large-scale comparison of different predictors over larger geographic areas than those currently studied is lacking to determine which predictors are the most effective. In addition, it provides insight into techniques of Machine Learning used for future predictive models, as well as general workflow for Machine Learning projects of dengue fever.

Mathematical models for dengue fever epidemiology: A 10-year systematic review

Mathematical models have a long history in epidemiological research, and as the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. Mathematical models describing dengue fever epidemiological dynamics are found back from 1970. Dengue fever is a viral mosquito-borne infection caused by four antigenically related but distinct serotypes (DENV-1 to DENV-4). With 2.5 billion people at risk of acquiring the infection, it is a major international public health concern. Although most of the cases are asymptomatic or mild, the disease immunological response is complex, with severe disease linked to the antibody-dependent enhancement (ADE) - a disease augmentation phenomenon where pre-existing antibodies to previous dengue infection do not neutralize but rather enhance the new infection. Here, we present a 10-year systematic review on mathematical models for dengue fever epidemiology. Specifically, we review multi-strain frameworks describing host-to-host and vector-host transmission models and within-host models describing viral replication and the respective immune response. Following a detailed literature search in standard scientific databases, different mathematical models in terms of their scope, analytical approach and structural form, including model validation and parameter estimation using empirical data, are described and analyzed. Aiming to identify a consensus on infectious diseases modeling aspects that can contribute to public health authorities for disease control, we revise the current understanding of epidemiological and immunological factors influencing the transmission dynamics of dengue. This review provide insights on general features to be considered to model aspects of real-world public health problems, such as the current epidemiological scenario we are living in.

Reduced dengue incidence during the COVID-19 movement restrictions in Sri Lanka from March 2020 to April 2021

Background

Dengue is the major mosquito-borne disease in Sri Lanka. After its first detection in January 2020, COVID-19 has become the major health issue in Sri Lanka. The impact of public health measures, notably restrictions on movement of people to curb COVID-19 transmission, on the incidence of dengue during the period March 2020 to April 2021 was investigated.

Methods

The incidence of dengue and COVID-19, rainfall and the public movement restrictions implemented to contain COVID-19 transmission were obtained from Sri Lanka government sources. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to predict the monthly dengue incidence from March 2020 to April 2021 for each of the country’s 25 districts based on five years of pre-pandemic data, and compared with the actual recorded incidence of dengue during this period. Ovitrap collections of Aedes larvae were performed in Jaffna city in the Jaffna district from August 2020 to April 2021 and the findings compared with similar collections made in the pre-pandemic period from March 2019 to December 2019.

Results

The recorded numbers of dengue cases for every month from March 2020 to April 2021 in the whole country and for all 25 districts over the same period were lower than the numbers of dengue cases predicted from data for the five years (2015–2019) immediately preceding the COVID-19 pandemic. The number of dengue cases recorded nationwide represented a 74% reduction from the predicted number of dengue cases for the March 2020 to April 2021 period. The numbers of Aedes larvae collected from ovitraps per month were reduced by 88.6% with a lower proportion of Ae. aegypti than Ae. albopictus in Jaffna city from August 2020 until April 2021 compared with March 2019 to December 2019.

Conclusion

Public health measures that restricted movement of people, closed schools, universities and offices to contain COVID-19 transmission unexpectedly led to a significant reduction in the reported numbers of dengue cases in Sri Lanka. This contrasts with findings reported from Singapore. The differences between the two tropical islands have significant implications for the epidemiology of dengue. Reduced access to blood meals and lower vector densities, particularly of Ae. aegypti, resulting from the restrictions on movement of people, are suggested to have contributed to the lower dengue incidence in Sri Lanka.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-12726-8.

Regional Variation in Dengue Virus Serotypes in Sri Lanka and Its Clinical and Epidemiological Relevance

Dengue is a significant health concern in Sri Lanka, but diagnosis of the infecting dengue virus (DENV) serotype has hitherto been largely restricted to the Colombo district in the western province. Salinity tolerant Aedes vectors are present in the island's northern Jaffna peninsula, which is undergoing rapid groundwater salinization. Virus serotypes were determined by RT-qPCR in 107 and 112 patients diagnosed by NS1 antigen positivity from the Jaffna district in 2018 and 2019, respectively, and related to clinical characteristics. DENV1 and DENV2 were the most common serotypes in both years. Infections with multiple serotypes were not detected. DENV1 was significantly more prevalent in 2019 than 2018, while DENV3 was significantly more prevalent in 2018 than 2019 among the Jaffna patients. Limited genomic sequencing identified DENV1 genotype-I and DENV3 genotype-I in Jaffna patients in 2018. Dengue was more prevalent in working age persons and males among the serotyped Jaffna patients. DENV1 and DENV2 were the predominant serotypes in 2019 in the Colombo district. However, DENV1 and DENV3 were significantly more prevalent in Colombo compared with Jaffna in 2019. The differences in the prevalence of DENV1 and DENV3 between the Jaffna and Colombo districts in 2019 have implications for dengue epidemiology and vaccination. Salinity-tolerant Aedes vector strains, widespread in the Jaffna peninsula, may have contributed to differences in serotype prevalence compared with the Colombo district in 2019. Significant associations were not identified between virus serotypes and clinical characteristics among Jaffna patients.

Discrete epidemic models with two time scales

The main aim of the work is to present a general class of two time scales discrete-time epidemic models. In the proposed framework the disease dynamics is considered to act on a slower time scale than a second different process that could represent movements between spatial locations, changes of individual activities or behaviors, or others. To include a sufficiently general disease model, we first build up from first principles a discrete-time susceptible-exposed-infectious-recovered-susceptible (SEIRS) model and characterize the eradication or endemicity of the disease with the help of its basic reproduction number R 0 . Then, we propose a general full model that includes sequentially the two processes at different time scales and proceed to its analysis through a reduced model. The basic reproduction number R ‾ 0 of the reduced system gives a good approximation of R 0 of the full model since it serves at analyzing its asymptotic behavior. As an illustration of the proposed general framework, it is shown that there exist conditions under which a locally endemic disease, considering isolated patches in a metapopulation, can be eradicated globally by establishing the appropriate movements between patches.

Effect of social isolation in dengue cases in the state of Sao Paulo, Brazil: An analysis during the COVID-19 pandemic

BACKGROUND

Studies have shown that human mobility is an important factor in dengue epidemiology. Changes in mobility resulting from COVID-19 pandemic set up a real-life situation to test this hypothesis. Our objective was to evaluate the effect of reduced mobility due to this pandemic in the occurrence of dengue in the state of São Paulo, Brazil.

METHOD

It is an ecological study of time series, developed between January and August 2020. We use the number of confirmed dengue cases and residential mobility, on a daily basis, from secondary information sources. Mobility was represented by the daily percentage variation of residential population isolation, obtained from the Google database. We modeled the relationship between dengue occurrence and social distancing by negative binomial regression, adjusted for seasonality. We represent the social distancing dichotomously (isolation versus no isolation) and consider lag for isolation from the dates of occurrence of dengue.

RESULTS

The risk of dengue decreased around 9.1% (95% CI: 14.2 to 3.7) in the presence of isolation, considering a delay of 20 days between the degree of isolation and the dengue first symptoms.

CONCLUSIONS

We have shown that mobility can play an important role in the epidemiology of dengue and should be considered in surveillance and control activities.

The impact of dengue illness on social distancing and caregiving behavior

BACKGROUND

Human mobility among residential locations can drive dengue virus (DENV) transmission dynamics. Recently, it was shown that individuals with symptomatic DENV infection exhibit significant changes in their mobility patterns, spending more time at home during illness. This change in mobility is predicted to increase the risk of acquiring infection for those living with or visiting the ill individual. It has yet to be considered, however, whether social contacts are also changing their mobility, either by socially distancing themselves from the infectious individual or increasing contact to help care for them. Social, or physical, distancing and caregiving could have diverse yet important impacts on DENV transmission dynamics; therefore, it is necessary to better understand the nature and frequency of these behaviors including their effect on mobility.

METHODOLOGY AND PRINCIPAL FINDINGS

Through community-based febrile illness surveillance and RT-PCR infection confirmation, 67 DENV positive (DENV+) residents were identified in the city of Iquitos, Peru. Using retrospective interviews, data were collected on visitors and home-based care received during the illness. While 15% of participants lost visitors during their illness, 22% gained visitors; overall, 32% of all individuals (particularly females) received visitors while symptomatic. Caregiving was common (90%), particularly caring by housemates (91%) and caring for children (98%). Twenty-eight percent of caregivers changed their behavior enough to have their work (and, likely, mobility patterns) affected. This was significantly more likely when caring for individuals with low "health-related quality of well-being" during illness (Fisher's Exact, p = 0.01).

CONCLUSIONS/SIGNIFICANCE

Our study demonstrates that social contacts of individuals with dengue modify their patterns of visitation and caregiving. The observed mobility changes could impact a susceptible individual's exposure to virus or a presymptomatic/clinically inapparent individual's contribution to onward transmission. Accounting for changes in social contact mobility is imperative in order to get a more accurate understanding of DENV transmission.

Implications of the COVID-19 Lockdown on Dengue Transmission in Malaysia

We aim to investigate the effect of large-scale human movement restrictions during the COVID-19 lockdown on both the dengue transmission and vector occurrences. This study compared the weekly dengue incidences during the period of lockdown to the previous years (2015 to 2019) and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model that expected no movement restrictions. We found that the trend of dengue incidence during the first two weeks (stage 1) of lockdown decreased significantly with the incidences lower than the lower confidence level (LCL) of SARIMA. By comparing the magnitude of the gradient of decrease, the trend is 319% steeper than the trend observed in previous years and 650% steeper than the simulated model, indicating that the control of population movement did reduce dengue transmission. However, starting from stage 2 of lockdown, the dengue incidences demonstrated an elevation and earlier rebound by four weeks and grew with an exponential pattern. We revealed that Aedes albopictus is the predominant species and demonstrated a strong correlation with the locally reported dengue incidences, and therefore we proposed the possible diffusive effect of the vector that led to a higher acceleration of incidence rate.

The impact of novel coronavirus (2019-nCoV) pandemic movement control order (MCO) on dengue cases in Peninsular Malaysia

This study has highlighted the trend of recently-reported dengue cases after the implementation of the Movement Control Orders (MCOs) caused due to COVID-19 pandemic in Malaysia. The researchers used the dengue surveillance data published by the Malaysian Ministry of Health during the 3 phases of MCO (which ranged between 17th March 2020 and 28^th April 2020) was used for determining the cumulative number of dengue patients. Thereafter, the dengue cases were mapped using the Geographical Information System (GIS). The results indicated that during the 42 days of MCO in Peninsular Malaysia, 11,242 total cases of dengue were reported. The daily trend of the dengue cases showed a decrease from 7268 cases that occurred before the MCOs to 4662 dengue cases that occurred during the initial 14 days of the COVID-19 pandemic (i.e., MCO I), to 3075 cases occurring during the MCO II and 3505 dengue cases noted during MCO III. The central peninsular region showed a maximal decrease in new dengue cases (52.62%), followed by the northern peninsular region (1.89%); eastern coastal region (1.25%) and the southern peninsular region (1.14%) during the initial MCO implementation. However, an increase in the new dengue cases was noted during the MCO III period, wherein all states showed an increase in the new dengue cases as compared during MCO II. The decrease in the pattern was not solely based on the MCO, hence, further investigation is necessary after considering different influencing factors. These results have important implication for future large-scale risk assessment, planning and hazard mitigation on dengue management.

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Dengue has been described as a “silent killer” amidst the COVID-19 pandemic.

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The MCOs implemented by the Malaysian government to break the COVID-19 infection chain have positively affected the onset of new dengue cases.

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The highest reduction of new dengue cases were reported in the central peninsular region (52.62%), followed by the northern peninsular region (1.89%), eastern coastal region (1.25%) and the southern peninsular region (1.14%).

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New dengue cases increased during the MCO3 period, with the increase of reported dengue cases in all states in Peninsular Malaysia.

An SIS model for the epidemic dynamics with two phases of the human day-to-day activity

An SIS model is analyzed to consider the contribution of community structure to the risk of the spread of a transmissible disease. We focus on the human day-to-day activity introduced by commuting to a central place for the social activity. We assume that the community is classified into two subpopulations: commuter and non-commuter, of which the commuter has two phases of the day-to-day activity: private and social. Further we take account of the combination of contact patterns in two phases, making use of mass-action and ratio-dependent types for the infection force. We investigate the dependence of the basic reproduction number on the commuter ratio and the daily expected duration at the social phase as essential factors characterizing the community structure, and show that the dependence is significantly affected by the combination of contact patterns, and that the difference in the commuter ratio could make the risk of the spread of a transmissible disease significantly different.

An SIS model for the epidemic dynamics with two phases of the human day-to-day activity

An SIS model is analyzed to consider the contribution of community structure to the risk of the spread of a transmissible disease. We focus on the human day-to-day activity introduced by commuting to a central place for the social activity. We assume that the community is classified into two subpopulations: commuter and non-commuter, of which the commuter has two phases of the day-to-day activity: private and social. Further we take account of the combination of contact patterns in two phases, making use of mass-action and ratio-dependent types for the infection force. We investigate the dependence of the basic reproduction number on the commuter ratio and the daily expected duration at the social phase as essential factors characterizing the community structure, and show that the dependence is significantly affected by the combination of contact patterns, and that the difference in the commuter ratio could make the risk of the spread of a transmissible disease significantly different.

Dengue illness impacts daily human mobility patterns in Iquitos, Peru

Background

Human mobility plays a central role in shaping pathogen transmission by generating spatial and/or individual variability in potential pathogen-transmitting contacts. Recent research has shown that symptomatic infection can influence human mobility and pathogen transmission dynamics. Better understanding the complex relationship between symptom severity, infectiousness, and human mobility requires quantification of movement patterns throughout infectiousness. For dengue virus (DENV), human infectiousness peaks 0–2 days after symptom onset, making it paramount to understand human movement patterns from the beginning of illness.

Methodology and principal findings

Through community-based febrile surveillance and RT-PCR assays, we identified a cohort of DENV+ residents of the city of Iquitos, Peru (n = 63). Using retrospective interviews, we measured the movements of these individuals when healthy and during each day of symptomatic illness. The most dramatic changes in mobility occurred during the first three days after symptom onset; individuals visited significantly fewer locations (Wilcoxon test, p = 0.017) and spent significantly more time at home (Wilcoxon test, p = 0.005), compared to when healthy. By 7–9 days after symptom onset, mobility measures had returned to healthy levels. Throughout an individual’s symptomatic period, the day of illness and their subjective sense of well-being were the most significant predictors for the number of locations and houses they visited.

Conclusions/Significance

Our study is one of the first to collect and analyze human mobility data at a daily scale during symptomatic infection. Accounting for the observed changes in human mobility throughout illness will improve understanding of the impact of disease on DENV transmission dynamics and the interpretation of public health-based surveillance data.

Dengue is the most important mosquito-borne viral disease of humans worldwide. Due to the limited mobility of the mosquitoes that transmit dengue virus, human mobility can be a key to both understanding an individual’s exposure to the virus and explaining the spread of dengue throughout a population. Accurate disease models should include human mobility; however, changes in human movement patterns due to the presence of symptoms need to be taken into account. We quantified the impact of symptom presence on human mobility throughout the infectious period by analyzing a dataset on the daily movements of dengue virus infected individuals. Accounting for these changing patterns of mobility will improve understanding of the complex relationship between symptom severity, human movement, and dengue virus transmission. Furthermore, dengue transmission models that incorporate symptom-driven mobility changes can be used to evaluate scenarios and strategies for disease prevention.

Seroprevalence of dengue in school children in Mexico ages 6-17 years, 2016

Background

Dengue is the most important arboviral disease in the world. Seroprevalence has been proposed as a marker of endemicity, however, studies are scarce.

Methods

We conducted a cross-sectional, stratified cluster, random sample study to measure the seroprevalence of antibodies to dengue virus (DENV) in Mexico. The target population was school children ages 6–17 y from 22 endemic states in Mexico, clustered in four regions: Pacific, South-Central, Southeast and Low.

Results

A total of 2134 subjects provided blood samples for immunoglobulin G antibody detection in serum by enzyme-linked immunosorbent assay. Overall, the seroprevalence of antibodies against DENV was 33.5% (95% confidence interval [CI] 27.5 to 40.1). The Southeast had the highest regional seroprevalence, reaching 70.9% (95% CI 60.3 to 79.7). Seroprevalence was higher in older children in the Southeast region: 62.1% (95% CI 46.9 to 75.2) in children 6–8 y and 82.6% (95% CI 73.8 to 88.9) in 13–17 years old (y). However, this was not consistent in all regions. Seroprevalence was associated with dengue incidence.

Conclusions

DENV seroprevalence in Mexico was found to be heterogeneous at the country, regional and state levels. Seroprevalence was linked to long-term exposure and did not adequately reflect recent patterns of transmission, suggesting that utilization of a single epidemiological indicator to define endemic regions should be avoided.

Assessing the effects of daily commuting in two-patch dengue dynamics: A case study of Cali, Colombia

There are many infectious diseases that can be spread by daily commuting of people and dengue fever is one of them. The absence of vaccine and irregularities in ongoing vector control programs make this disease the most frequent and persistent in many tropical and subtropical regions of the world. This paper targets to access the effects of daily commuting on dengue transmission dynamics by using a deterministic two-patch model fitted to observed data gathered in Cali, Colombia where dengue fever is highly persistent and exhibits endemo-epidemic patterns. The two-patch dengue transmission model with daily communing of human residents between patches (that is, between the city and its suburban areas) is presented using the concept of residence times, which certainly affect the disease transmission rates by inducing variability in human population sizes and vectorial densities at each patch. The same modeling framework is applied to two primary scenarios (epidemic outbreaks and endemic persistence of the disease) and for each scenario two coupling cases (one-way and asymmetric commuting) with different inflow and outflow intensities are analyzed. The concept of effective vectorial density, introduced in this paper, allows to explain in very simple terms why the daily commuting affects quite differently the dengue morbidity among human residents in both patches. In particular, residents of the patch with a greater share of incoming than outgoing commuters may actually "benefit" from inflow of daily commuter by avoiding a considerable number of infections. However, residents of the patch with a greater share of outgoing than incoming commuters, especially those who stay at home patch, incur more risk of getting infected. Additionally, the model shows that daily commuting enhance the total number of human infections acquired in both patches and may even provoke an epidemic outbreak in one patch while moderately lowering the level of the disease persistence in another patch.

Multi-patch and multi-group epidemic models: a new framework

We develop a multi-patch and multi-group model that captures the dynamics of an infectious disease when the host is structured into an arbitrary number of groups and interacts into an arbitrary number of patches where the infection takes place. In this framework, we model host mobility that depends on its epidemiological status, by a Lagrangian approach. This framework is applied to a general SEIRS model and the basic reproduction number [Formula: see text] is derived. The effects of heterogeneity in groups, patches and mobility patterns on [Formula: see text] and disease prevalence are explored. Our results show that for a fixed number of groups, the basic reproduction number increases with respect to the number of patches and the host mobility patterns. Moreover, when the mobility matrix of susceptible individuals is of rank one, the basic reproduction number is explicitly determined and was found to be independent of the latter if the matrix is also stochastic. The cases where mobility matrices are of rank one capture important modeling scenarios. Additionally, we study the global analysis of equilibria for some special cases. Numerical simulations are carried out to showcase the ramifications of mobility pattern matrices on disease prevalence and basic reproduction number.

Analysis of spatial mobility in subjects from a Dengue endemic urban locality in Morelos State, Mexico

INTRODUCTION

Mathematical models and field data suggest that human mobility is an important driver for Dengue virus transmission. Nonetheless little is known on this matter due the lack of instruments for precise mobility quantification and study design difficulties.

MATERIALS AND METHODS

We carried out a cohort-nested, case-control study with 126 individuals (42 cases, 42 intradomestic controls and 42 population controls) with the goal of describing human mobility patterns of recently Dengue virus-infected subjects, and comparing them with those of non-infected subjects living in an urban endemic locality. Mobility was quantified using a GPS-data logger registering waypoints at 60-second intervals for a minimum of 15 natural days.

RESULTS

Although absolute displacement was highly biased towards the intradomestic and peridomestic areas, occasional displacements exceeding a 100-Km radius from the center of the studied locality were recorded for all three study groups and individual displacements were recorded traveling across six states from central Mexico. Additionally, cases had a larger number of visits out of the municipality´s administrative limits when compared to intradomestic controls (cases: 10.4 versus intradomestic controls: 2.9, p = 0.0282). We were able to identify extradomestic places within and out of the locality that were independently visited by apparently non-related infected subjects, consistent with houses, working and leisure places.

CONCLUSIONS

Results of this study show that human mobility in a small urban setting exceeded that considered by local health authority's administrative limits, and was different between recently infected and non-infected subjects living in the same household. These observations provide important insights about the role that human mobility may have in Dengue virus transmission and persistence across endemic geographic areas that need to be taken into account when planning preventive and control measures. Finally, these results are a valuable reference when setting the parameters for future mathematical modeling studies.