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11 pages, 1331 KiB

Open AccessArticle

SEAHIR: A Specialized Compartmental Model for COVID-19

by Alexandros Leontitsis, Abiola Senok, Alawi Alsheikh-Ali, Younus Al Nasser, Tom Loney and Aamena Alshamsi

Int. J. Environ. Res. Public Health 2021, 18(5), 2667; https://doi.org/10.3390/ijerph18052667 - 6 Mar 2021

Cited by 41 | Viewed by 4210

Abstract

The SEIR (Susceptible-Exposed-Infected-Removed) model is widely used in epidemiology to mathematically model the spread of infectious diseases with incubation periods. However, the SEIR model prototype is generic and not able to capture the unique nature of a novel viral pandemic such as SARS-CoV-2. [...] Read more.

The SEIR (Susceptible-Exposed-Infected-Removed) model is widely used in epidemiology to mathematically model the spread of infectious diseases with incubation periods. However, the SEIR model prototype is generic and not able to capture the unique nature of a novel viral pandemic such as SARS-CoV-2. We have developed and tested a specialized version of the SEIR model, called SEAHIR (Susceptible-Exposed-Asymptomatic-Hospitalized-Isolated-Removed) model. This proposed model is able to capture the unique dynamics of the COVID-19 outbreak including further dividing the Infected compartment into: (1) “Asymptomatic”, (2) “Isolated” and (3) “Hospitalized” to delineate the transmission specifics of each compartment and forecast healthcare requirements. The model also takes into consideration the impact of non-pharmaceutical interventions such as physical distancing and different testing strategies on the number of confirmed cases. We used a publicly available dataset from the United Arab Emirates (UAE) as a case study to optimize the main parameters of the model and benchmarked it against the historical number of cases. The SEAHIR model was used by decision-makers in Dubai’s COVID-19 Command and Control Center to make timely decisions on developing testing strategies, increasing healthcare capacity, and implementing interventions to contain the spread of the virus. The novel six-compartment SEAHIR model could be utilized by decision-makers and researchers in other countries for current or future pandemics. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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19 pages, 2983 KiB

Open AccessArticle

Fecal Indicator Bacteria Data to Characterize Drinking Water Quality in Low-Resource Settings: Summary of Current Practices and Recommendations for Improving Validity

by Mustafa Sikder, Elena N. Naumova, Anthonia O. Ogudipe, Mateo Gomez and Daniele Lantagne

Int. J. Environ. Res. Public Health 2021, 18(5), 2353; https://doi.org/10.3390/ijerph18052353 - 28 Feb 2021

Cited by 2 | Viewed by 4047

Abstract

Fecal indicator bacteria (FIB) values are widely used to assess microbial contamination in drinking water and to advance the modeling of infectious disease risks. The membrane filtration (MF) testing technique for FIB is widely adapted for use in low- and middle-income countries (LMICs). [...] Read more.

Fecal indicator bacteria (FIB) values are widely used to assess microbial contamination in drinking water and to advance the modeling of infectious disease risks. The membrane filtration (MF) testing technique for FIB is widely adapted for use in low- and middle-income countries (LMICs). We conducted a systematic literature review on the use of MF-based FIB data in LMICs and summarized statistical methods from 172 articles. We then applied the commonly used statistical methods from the review on publicly available datasets to illustrate how data analysis methods affect FIB results and interpretation. Our findings indicate that standard methods for processing samples are not widely reported, the selection of statistical tests is rarely justified, and, depending on the application, statistical methods can change risk perception and present misleading results. These results raise concerns about the validity of FIB data collection, analysis, and presentation in LMICs. To improve evidence quality, we propose a FIB data reporting checklist to use as a reminder for researchers and practitioners. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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21 pages, 6659 KiB

Open AccessArticle

Effects of Data Aggregation on Time Series Analysis of Seasonal Infections

by Tania M. Alarcon Falconi, Bertha Estrella, Fernando Sempértegui and Elena N. Naumova

Int. J. Environ. Res. Public Health 2020, 17(16), 5887; https://doi.org/10.3390/ijerph17165887 - 13 Aug 2020

Cited by 21 | Viewed by 4495

Abstract

Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We [...] Read more.

Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We present a critical thinking process for making decisions about data aggregation in time series analysis of seasonal infections. We systematically build a harmonic regression model to characterize peak timing and amplitude of three respiratory and enteric infections that have different seasonal patterns and incidence. We show that irregularities introduced when aggregating data must be controlled during modeling to prevent erroneous results. Aggregation irregularities had a minimal impact on the estimates of trend, amplitude, and peak timing for daily and weekly data regardless of the disease. However, estimates of peak timing of the more common infections changed by as much as 2.5 months when controlling for monthly data irregularities. Building a systematic model that controls for data irregularities is essential to accurately characterize temporal patterns of infections. With the urgent need to characterize temporal patterns of novel infections, such as COVID-19, this tutorial is timely and highly valuable for experts in many disciplines. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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13 pages, 1949 KiB

Open AccessArticle

Modelling the Effectiveness of Epidemic Control Measures in Preventing the Transmission of COVID-19 in Malaysia

by Balvinder Singh Gill, Vivek Jason Jayaraj, Sarbhan Singh, Sumarni Mohd Ghazali, Yoon Ling Cheong, Nuur Hafizah Md Iderus, Bala Murali Sundram, Tahir Bin Aris, Hishamshah Mohd Ibrahim, Boon Hao Hong and Jane Labadin

Int. J. Environ. Res. Public Health 2020, 17(15), 5509; https://doi.org/10.3390/ijerph17155509 - 30 Jul 2020

Cited by 41 | Viewed by 7691

Abstract

Malaysia is currently facing an outbreak of COVID-19. We aim to present the first study in Malaysia to report the reproduction numbers and develop a mathematical model forecasting COVID-19 transmission by including isolation, quarantine, and movement control measures. We utilized a susceptible, exposed, [...] Read more.

Malaysia is currently facing an outbreak of COVID-19. We aim to present the first study in Malaysia to report the reproduction numbers and develop a mathematical model forecasting COVID-19 transmission by including isolation, quarantine, and movement control measures. We utilized a susceptible, exposed, infectious, and recovered (SEIR) model by incorporating isolation, quarantine, and movement control order (MCO) taken in Malaysia. The simulations were fitted into the Malaysian COVID-19 active case numbers, allowing approximation of parameters consisting of probability of transmission per contact (β), average number of contacts per day per case (ζ), and proportion of close-contact traced per day (q). The effective reproduction number (R_t) was also determined through this model. Our model calibration estimated that (β), (ζ), and (q) were 0.052, 25 persons, and 0.23, respectively. The (R_t) was estimated to be 1.68. MCO measures reduce the peak number of active COVID-19 cases by 99.1% and reduce (ζ) from 25 (pre-MCO) to 7 (during MCO). The flattening of the epidemic curve was also observed with the implementation of these control measures. We conclude that isolation, quarantine, and MCO measures are essential to break the transmission of COVID-19 in Malaysia. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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19 pages, 5602 KiB

Open AccessArticle

Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea

by Seungwon Jung, Jaeuk Moon and Eenjun Hwang

Int. J. Environ. Res. Public Health 2020, 17(13), 4872; https://doi.org/10.3390/ijerph17134872 - 6 Jul 2020

Cited by 5 | Viewed by 3057

Abstract

For a long time, various epidemics, such as lower respiratory infections and diarrheal diseases, have caused serious social losses and costs. Various methods for analyzing infectious disease occurrences have been proposed for effective prevention and proactive response to reduce such losses and costs. [...] Read more.

For a long time, various epidemics, such as lower respiratory infections and diarrheal diseases, have caused serious social losses and costs. Various methods for analyzing infectious disease occurrences have been proposed for effective prevention and proactive response to reduce such losses and costs. However, the results of the occurrence analyses were limited because numerous factors affect the outbreak of infectious diseases and there are complex interactions between these factors. To alleviate this limitation, we propose a cluster-based analysis scheme of infectious disease occurrences that can discover commonalities or differences between clusters by grouping elements with similar occurrence patterns. To do this, we collect and preprocess infectious disease occurrence data according to time, region, and disease. Then, we construct a tensor for the data and apply Tucker decomposition to extract latent features in the dimensions of time, region, and disease. Based on these latent features, we conduct k-means clustering and analyze the results for each dimension. To demonstrate the effectiveness of this scheme, we conduct a case study on data from South Korea and report some of the results. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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19 pages, 408 KiB

Open AccessArticle

A Model for the Spread of Infectious Diseases in a Region

by Elizabeth Hunter, Brian Mac Namee and John D. Kelleher

Int. J. Environ. Res. Public Health 2020, 17(9), 3119; https://doi.org/10.3390/ijerph17093119 - 30 Apr 2020

Cited by 14 | Viewed by 3746

Abstract

In understanding the dynamics of the spread of an infectious disease, it is important to understand how a town’s place in a network of towns within a region will impact how the disease spreads to that town and from that town. In this [...] Read more.

In understanding the dynamics of the spread of an infectious disease, it is important to understand how a town’s place in a network of towns within a region will impact how the disease spreads to that town and from that town. In this article, we take a model for the spread of an infectious disease in a single town and scale it up to simulate a region containing multiple towns. The model is validated by looking at how adding additional towns and commuters influences the outbreak in a single town. We then look at how the centrality of a town within a network influences the outbreak. Our main finding is that the commuters coming into a town have a greater effect on whether an outbreak will spread to a town than the commuters going out. The findings on centrality of a town and how it influences an outbreak could potentially be used to help influence future policy and intervention strategies such as school closure policies. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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11 pages, 1918 KiB

Open AccessArticle

Analysis of the Healthcare MERS-CoV Outbreak in King Abdulaziz Medical Center, Riyadh, Saudi Arabia, June–August 2015 Using a SEIR Ward Transmission Model

by Tamer Oraby, Michael G. Tyshenko, Hanan H. Balkhy, Yasar Tasnif, Adriana Quiroz-Gaspar, Zeinab Mohamed, Ayesha Araya, Susie Elsaadany, Eman Al-Mazroa, Mohammed A. Alhelail, Yaseen M. Arabi and Mustafa Al-Zoughool

Int. J. Environ. Res. Public Health 2020, 17(8), 2936; https://doi.org/10.3390/ijerph17082936 - 23 Apr 2020

Cited by 14 | Viewed by 3977

Abstract

Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging zoonotic coronavirus that has a tendency to cause significant healthcare outbreaks among patients with serious comorbidities. We analyzed hospital data from the MERS-CoV outbreak in King Abdulaziz Medical Center, Riyadh, Saudi Arabia, June–August 2015 [...] Read more.

Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging zoonotic coronavirus that has a tendency to cause significant healthcare outbreaks among patients with serious comorbidities. We analyzed hospital data from the MERS-CoV outbreak in King Abdulaziz Medical Center, Riyadh, Saudi Arabia, June–August 2015 using the susceptible-exposed-infectious-recovered (SEIR) ward transmission model. The SEIR compartmental model considers several areas within the hospital where transmission occurred. We use a system of ordinary differential equations that incorporates the following units: emergency department (ED), out-patient clinic, intensive care unit, and hospital wards, where each area has its own carrying capacity and distinguishes the transmission by three individuals in the hospital: patients, health care workers (HCW), or mobile health care workers. The emergency department, as parameterized has a large influence over the epidemic size for both patients and health care workers. Trend of the basic reproduction number (R₀), which reached a maximum of 1.39 at the peak of the epidemic and declined to 0.92 towards the end, shows that until added hospital controls are introduced, the outbreak would continue with sustained transmission between wards. Transmission rates where highest in the ED, and mobile HCWs were responsible for large part of the outbreak. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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19 pages, 1189 KiB

Open AccessArticle

Integer Versus Fractional Order SEIR Deterministic and Stochastic Models of Measles

by Md Rafiul Islam, Angela Peace, Daniel Medina and Tamer Oraby

Int. J. Environ. Res. Public Health 2020, 17(6), 2014; https://doi.org/10.3390/ijerph17062014 - 18 Mar 2020

Cited by 31 | Viewed by 4165

Abstract

In this paper, we compare the performance between systems of ordinary and (Caputo) fractional differential equations depicting the susceptible-exposed-infectious-recovered (SEIR) models of diseases. In order to understand the origins of both approaches as mean-field approximations of integer and fractional stochastic processes, we introduce [...] Read more.

In this paper, we compare the performance between systems of ordinary and (Caputo) fractional differential equations depicting the susceptible-exposed-infectious-recovered (SEIR) models of diseases. In order to understand the origins of both approaches as mean-field approximations of integer and fractional stochastic processes, we introduce the fractional differential equations (FDEs) as approximations of some type of fractional nonlinear birth and death processes. Then, we examine validity of the two approaches against empirical courses of epidemics; we fit both of them to case counts of three measles epidemics that occurred during the pre-vaccination era in three different locations. While ordinary differential equations (ODEs) are commonly used to model epidemics, FDEs are more flexible in fitting empirical data and theoretically offer improved model predictions. The question arises whether, in practice, the benefits of using FDEs over ODEs outweigh the added computational complexities. While important differences in transient dynamics were observed, the FDE only outperformed the ODE in one of out three data sets. In general, FDE modeling approaches may be worth it in situations with large refined data sets and good numerical algorithms. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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13 pages, 441 KiB

Open AccessArticle

Forecasting Flu Activity in the United States: Benchmarking an Endemic-Epidemic Beta Model

by Junyi Lu and Sebastian Meyer

Int. J. Environ. Res. Public Health 2020, 17(4), 1381; https://doi.org/10.3390/ijerph17041381 - 21 Feb 2020

Cited by 14 | Viewed by 3679

Abstract

Accurate prediction of flu activity enables health officials to plan disease prevention and allocate treatment resources. A promising forecasting approach is to adapt the well-established endemic-epidemic modeling framework to time series of infectious disease proportions. Using U.S. influenza-like illness surveillance data over 18 [...] Read more.

Accurate prediction of flu activity enables health officials to plan disease prevention and allocate treatment resources. A promising forecasting approach is to adapt the well-established endemic-epidemic modeling framework to time series of infectious disease proportions. Using U.S. influenza-like illness surveillance data over 18 seasons, we assessed probabilistic forecasts of this new beta autoregressive model with proper scoring rules. Other readily available forecasting tools were used for comparison, including Prophet, (S)ARIMA and kernel conditional density estimation (KCDE). Short-term flu activity was equally well predicted up to four weeks ahead by the beta model with four autoregressive lags and by KCDE; however, the beta model runs much faster. Non-dynamic Prophet scored worst. Relative performance differed for seasonal peak prediction. Prophet produced the best peak intensity forecasts in seasons with standard epidemic curves; otherwise, KCDE outperformed all other methods. Peak timing was best predicted by SARIMA, KCDE or the beta model, depending on the season. The best overall performance when predicting peak timing and intensity was achieved by KCDE. Only KCDE and naive historical forecasts consistently outperformed the equal-bin reference approach for all test seasons. We conclude that the endemic-epidemic beta model is a performant and easy-to-implement tool to forecast flu activity a few weeks ahead. Real-time forecasting of the seasonal peak, however, should consider outputs of multiple models simultaneously, weighing their usefulness as the season progresses. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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14 pages, 2826 KiB

Open AccessArticle

Assessing Seasonality Variation with Harmonic Regression: Accommodations for Sharp Peaks

by Kavitha Ramanathan, Mani Thenmozhi, Sebastian George, Shalini Anandan, Balaji Veeraraghavan, Elena N. Naumova and Lakshmanan Jeyaseelan

Int. J. Environ. Res. Public Health 2020, 17(4), 1318; https://doi.org/10.3390/ijerph17041318 - 18 Feb 2020

Cited by 25 | Viewed by 5041

Abstract

The use of the harmonic regression model is well accepted in the epidemiological and biostatistical communities as a standard procedure to examine seasonal patterns in disease occurrence. While these models may provide good fit to periodic patterns with relatively symmetric rises and falls, [...] Read more.

The use of the harmonic regression model is well accepted in the epidemiological and biostatistical communities as a standard procedure to examine seasonal patterns in disease occurrence. While these models may provide good fit to periodic patterns with relatively symmetric rises and falls, for some diseases the incidence fluctuates in a more complex manner. We propose a two-step harmonic regression approach to improve the model fit for data exhibiting sharp seasonal peaks. To capture such specific behavior, we first build a basic model and estimate the seasonal peak. At the second step, we apply an extended model using sine and cosine transform functions. These newly proposed functions mimic a quadratic term in the harmonic regression models and thus allow us to better fit the seasonal spikes. We illustrate the proposed method using actual and simulated data and recommend the new approach to assess seasonality in a broad spectrum of diseases manifesting sharp seasonal peaks. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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17 pages, 2088 KiB

Open AccessArticle

Pathogen-Specific Impacts of the 2011–2012 La Niña-Associated Floods on Enteric Infections in the MAL-ED Peru Cohort: A Comparative Interrupted Time Series Analysis

by Josh Colston, Maribel Paredes Olortegui, Benjamin Zaitchik, Pablo Peñataro Yori, Gagandeep Kang, Tahmeed Ahmed, Pascal Bessong, Esto Mduma, Zulfiqar Bhutta, Prakash Sunder Shrestha, Aldo Lima and Margaret Kosek

Int. J. Environ. Res. Public Health 2020, 17(2), 487; https://doi.org/10.3390/ijerph17020487 - 12 Jan 2020

Cited by 30 | Viewed by 5470

Abstract

Extreme floods pose multiple direct and indirect health risks. These risks include contamination of water, food, and the environment, often causing outbreaks of diarrheal disease. Evidence regarding the effects of flooding on individual diarrhea-causing pathogens is limited, but is urgently needed in order [...] Read more.

Extreme floods pose multiple direct and indirect health risks. These risks include contamination of water, food, and the environment, often causing outbreaks of diarrheal disease. Evidence regarding the effects of flooding on individual diarrhea-causing pathogens is limited, but is urgently needed in order to plan and implement interventions and prioritize resources before climate-related disasters strike. This study applied a causal inference approach to data from a multisite study that deployed broadly inclusive diagnostics for numerous high-burden common enteropathogens. Relative risks (RRs) of infection with each pathogen during a flooding disaster that occurred at one of the sites—Loreto, Peru—were calculated from generalized linear models using a comparative interrupted time series framework with the other sites as a comparison group and adjusting for background seasonality. During the early period of the flood, increased risk of heat-stable enterotoxigenic E. coli (ST-ETEC) was identified (RR = 1.73 [1.10, 2.71]) along with a decreased risk of enteric adenovirus (RR = 0.36 [0.23, 0.58]). During the later period of the flood, sharp increases in the risk of rotavirus (RR = 5.30 [2.70, 10.40]) and sapovirus (RR = 2.47 [1.79, 3.41]) were observed, in addition to increases in transmission of Shigella spp. (RR = 2.86 [1.81, 4.52]) and Campylobacter spp. (RR = 1.41 (1.01, 1.07). Genotype-specific exploratory analysis reveals that the rise in rotavirus transmission during the flood was likely due to the introduction of a locally atypical, non-vaccine (G2P[4]) strain of the virus. Policy-makers should target interventions towards these pathogens—including vaccines as they become available—in settings where vulnerability to flooding is high as part of disaster preparedness strategies, while investments in radical, transformative, community-wide, and locally-tailored water and sanitation interventions are also needed. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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20 pages, 6799 KiB

Open AccessArticle

A Gravity-Based Food Flow Model to Identify the Source of Foodborne Disease Outbreaks

by Tim Schlaich, Abigail L. Horn, Marcel Fuhrmann and Hanno Friedrich

Int. J. Environ. Res. Public Health 2020, 17(2), 444; https://doi.org/10.3390/ijerph17020444 - 9 Jan 2020

Cited by 10 | Viewed by 4285

Abstract

Computational traceback methodologies are important tools for investigations of widespread foodborne disease outbreaks as they assist investigators to determine the causative outbreak location and food item. In modeling the entire food supply chain from farm to fork, however, these methodologies have paid little [...] Read more.

Computational traceback methodologies are important tools for investigations of widespread foodborne disease outbreaks as they assist investigators to determine the causative outbreak location and food item. In modeling the entire food supply chain from farm to fork, however, these methodologies have paid little attention to consumer behavior and mobility, instead making the simplifying assumption that consumers shop in the area adjacent to their home location. This paper aims to fill this gap by introducing a gravity-based approach to model food-flows from supermarkets to consumers and demonstrating how models of consumer shopping behavior can be used to improve computational methodologies to infer the source of an outbreak of foodborne disease. To demonstrate our approach, we develop and calibrate a gravity model of German retail shopping behavior at the postal-code level. Modeling results show that on average about 70 percent of all groceries are sourced from non-home zip codes. The value of considering shopping behavior in computational approaches for inferring the source of an outbreak is illustrated through an application example to identify a retail brand source of an outbreak. We demonstrate a significant increase in the accuracy of a network-theoretic source estimator for the outbreak source when the gravity model is included in the food supply network compared with the baseline case when contaminated individuals are assumed to shop only in their home location. Our approach illustrates how gravity models can enrich computational inference models for identifying the source (retail brand, food item, location) of an outbreak of foodborne disease. More broadly, results show how gravity models can contribute to computational approaches to model consumer shopping interactions relating to retail food environments, nutrition, and public health. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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19 pages, 7481 KiB

Open AccessArticle

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

by Sucharita Gopal, Yaxiong Ma, Chen Xin, Joshua Pitts and Lawrence Were

Int. J. Environ. Res. Public Health 2019, 16(24), 5078; https://doi.org/10.3390/ijerph16245078 - 12 Dec 2019

Cited by 11 | Viewed by 7724

Abstract

The United Nations’ Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of [...] Read more.

The United Nations’ Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used—Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino–southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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24 pages, 2960 KiB

Open AccessArticle

Forecasting Seasonal Vibrio parahaemolyticus Concentrations in New England Shellfish

by Meghan A. Hartwick, Erin A. Urquhart, Cheryl A. Whistler, Vaughn S. Cooper, Elena N. Naumova and Stephen H. Jones

Int. J. Environ. Res. Public Health 2019, 16(22), 4341; https://doi.org/10.3390/ijerph16224341 - 7 Nov 2019

Cited by 20 | Viewed by 4095

Abstract

Seafood-borne Vibrio parahaemolyticus illness is a global public health issue facing resource managers and the seafood industry. The recent increase in shellfish-borne illnesses in the Northeast United States has resulted in the application of intensive management practices based on a limited understanding of [...] Read more.

Seafood-borne Vibrio parahaemolyticus illness is a global public health issue facing resource managers and the seafood industry. The recent increase in shellfish-borne illnesses in the Northeast United States has resulted in the application of intensive management practices based on a limited understanding of when and where risks are present. We aim to determine the contribution of factors that affect V. parahaemolyticus concentrations in oysters (Crassostrea virginica) using ten years of surveillance data for environmental and climate conditions in the Great Bay Estuary of New Hampshire from 2007 to 2016. A time series analysis was applied to analyze V. parahaemolyticus concentrations and local environmental predictors and develop predictive models. Whereas many environmental variables correlated with V. parahaemolyticus concentrations, only a few retained significance in capturing trends, seasonality and data variability. The optimal predictive model contained water temperature and pH, photoperiod, and the calendar day of study. The model enabled relatively accurate seasonality-based prediction of V. parahaemolyticus concentrations for 2014–2016 based on the 2007–2013 dataset and captured the increasing trend in extreme values of V. parahaemolyticus concentrations. The developed method enables the informative tracking of V. parahaemolyticus concentrations in coastal ecosystems and presents a useful platform for developing area-specific risk forecasting models. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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20 pages, 1838 KiB

Open AccessArticle

Rotavirus Seasonality: An Application of Singular Spectrum Analysis and Polyharmonic Modeling

by Olga K. Alsova, Valery B. Loktev and Elena N. Naumova

Int. J. Environ. Res. Public Health 2019, 16(22), 4309; https://doi.org/10.3390/ijerph16224309 - 6 Nov 2019

Cited by 10 | Viewed by 3561

Abstract

The dynamics of many viral infections, including rotaviral infections (RIs), are known to have a complex non-linear, non-stationary structure with strong seasonality indicative of virus and host sensitivity to environmental conditions. However, analytical tools suitable for the identification of seasonal peaks are limited. [...] Read more.

The dynamics of many viral infections, including rotaviral infections (RIs), are known to have a complex non-linear, non-stationary structure with strong seasonality indicative of virus and host sensitivity to environmental conditions. However, analytical tools suitable for the identification of seasonal peaks are limited. We introduced a two-step procedure to determine seasonal patterns in RI and examined the relationship between daily rates of rotaviral infection and ambient temperature in cold climates in three Russian cities: Chelyabinsk, Yekaterinburg, and Barnaul from 2005 to 2011. We described the structure of temporal variations using a new class of singular spectral analysis (SSA) models based on the “Caterpillar” algorithm. We then fitted Poisson polyharmonic regression (PPHR) models and examined the relationship between daily RI rates and ambient temperature. In SSA models, RI rates reached their seasonal peaks around 24 February, 5 March, and 12 March (i.e., the 55.17 ± 3.21, 64.17 ± 5.12, and 71.11 ± 7.48 day of the year) in Chelyabinsk, Yekaterinburg, and Barnaul, respectively. Yet, in all three cities, the minimum temperature was observed, on average, to be on 15 January, which translates to a lag between the peak in disease incidence and time of temperature minimum of 38–40 days for Chelyabinsk, 45–49 days in Yekaterinburg, and 56–59 days in Barnaul. The proposed approach takes advantage of an accurate description of the time series data offered by the SSA-model coupled with a straightforward interpretation of the PPHR model. By better tailoring analytical methodology to estimate seasonal features and understand the relationships between infection and environmental conditions, regional and global disease forecasting can be further improved. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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14 pages, 2302 KiB

Open AccessArticle

Spatiotemporal Patterns of Cholera Hospitalization in Vellore, India

by Aishwarya Venkat, Tania M. Alarcon Falconi, Melissa Cruz, Meghan A. Hartwick, Shalini Anandan, Naveen Kumar, Honorine Ward, Balaji Veeraraghavan and Elena N. Naumova

Int. J. Environ. Res. Public Health 2019, 16(21), 4257; https://doi.org/10.3390/ijerph16214257 - 2 Nov 2019

Cited by 3 | Viewed by 3983

Abstract

Systematically collected hospitalization records provide valuable insight into disease patterns and support comprehensive national infectious disease surveillance networks. Hospitalization records detailing patient’s place of residence (PoR) can be utilized to better understand a hospital’s case load and strengthen surveillance among mobile populations. This [...] Read more.

Systematically collected hospitalization records provide valuable insight into disease patterns and support comprehensive national infectious disease surveillance networks. Hospitalization records detailing patient’s place of residence (PoR) can be utilized to better understand a hospital’s case load and strengthen surveillance among mobile populations. This study examined geographic patterns of patients treated for cholera at a major hospital in south India. We abstracted 1401 laboratory-confirmed cases of cholera between 2000–2014 from logbooks and electronic health records (EHRs) maintained by the Christian Medical College (CMC) in Vellore, Tamil Nadu, India. We constructed spatial trend models and identified two distinct clusters of patient residence—one around Vellore (836 records (61.2%)) and one in Bengal (294 records (21.5%)). We further characterized differences in peak timing and disease trend among these clusters to identify differences in cholera exposure among local and visiting populations. We found that the two clusters differ by their patient profiles, with patients in the Bengal cluster being most likely older males traveling to Vellore. Both clusters show well-aligned seasonal peaks in mid-July, only one week apart, with similar downward trend and proportion of predominant O1 serotype. Large hospitals can thus harness EHRs for surveillance by utilizing patients’ PoRs to study disease patterns among resident and visitor populations. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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13 pages, 1812 KiB

Open AccessArticle

Seasonality of Rotavirus Hospitalizations at Costa Rica’s National Children’s Hospital in 2010–2015

by Katarina Ureña-Castro, Silvia Ávila, Mariela Gutierrez, Elena N. Naumova, Rolando Ulloa-Gutierrez and Alfredo Mora-Guevara

Int. J. Environ. Res. Public Health 2019, 16(13), 2321; https://doi.org/10.3390/ijerph16132321 - 30 Jun 2019

Cited by 18 | Viewed by 4087

Abstract

Rotavirus is a leading cause of acute diarrhea in children worldwide. Costa Rica recently started universal rotavirus vaccinations for infants with a two-dose schedule in February 2019. We aimed to study the seasonality of rotavirus during the pre-vaccination era. We retrospectively studied a [...] Read more.

Rotavirus is a leading cause of acute diarrhea in children worldwide. Costa Rica recently started universal rotavirus vaccinations for infants with a two-dose schedule in February 2019. We aimed to study the seasonality of rotavirus during the pre-vaccination era. We retrospectively studied a six-year period of hospital admissions due to rotavirus gastroenteritis. We estimated seasonal peak timing and relative intensities using trend-adjusted negative binomial regression models with the δ-method. We assessed the relationship between rotavirus cases and weather characteristics and estimated their effects for the current month, one-month prior and two months prior, by using Pearson correlation coefficients. A total of 798 cases were analyzed. Rotavirus cases predominated in the first five months of the year. On average, the peak of admissions occurred between late-February and early-March. During the seasonal peaks, the monthly count tended to increase 2.5–2.75 times above the seasonal nadir. We found the strongest negative association of monthly hospitalizations and joint percentiles of precipitation and minimal temperature at a lag of two months (R = −0.265, p = 0.027) and we detected correlations of −0.218, −0.223, and −0.226 (p < 0.05 for all three estimates) between monthly cases and the percentile of precipitation at lags 0, 1, and 2 months. In the warm tropical climate of Costa Rica, the increase in rotavirus hospitalizations coincided with dry and cold weather conditions with a two-month lag. The findings serve as the base for predictive modeling and estimation of the impact of a nation-wide vaccination campaign on pediatric rotaviral infection morbidity. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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19 pages, 6348 KiB

Open AccessArticle

Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates

by Pavel S. Stashevsky, Irina N. Yakovina, Tania M. Alarcon Falconi and Elena N. Naumova

Int. J. Environ. Res. Public Health 2019, 16(12), 2083; https://doi.org/10.3390/ijerph16122083 - 12 Jun 2019

Cited by 14 | Viewed by 3630

Abstract

The utility of agglomerative clustering methods for understanding dynamic systems that do not have a well-defined periodic structure has not yet been explored. We propose using this approach to examine the association between disease and weather parameters, to compliment the traditional harmonic regression [...] Read more.

The utility of agglomerative clustering methods for understanding dynamic systems that do not have a well-defined periodic structure has not yet been explored. We propose using this approach to examine the association between disease and weather parameters, to compliment the traditional harmonic regression models, and to determine specific meteorological conditions favoring high disease incidence. We utilized daily records on reported salmonellosis and non-specific enteritis, and four meteorological parameters (ambient temperature, dew point, humidity, and barometric pressure) in Barnaul, Russia in 2004–2011, maintained by the CliWaDIn database. The data structure was examined using the t-distributed stochastic neighbor embedding (t-SNE) method. The optimal number of clusters was selected based on Ward distance using the silhouette metric. The selected clusters were assessed with respect to their density and homogeneity. We detected that a well-defined cluster with high counts of salmonellosis occurred during warm summer days and unseasonably warm days in spring. We also detected a cluster with high counts of non-specific enteritis that occurred during unusually “very warm” winter days. The main advantage offered by the proposed technique is its ability to create a composite of meteorological conditions—a rule of thumb—to detect days favoring infectious outbreaks for a given location. These findings have major implications for understanding potential health impacts of climate change. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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Review

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12 pages, 335 KiB

Open AccessReview

How Seasonality of Malnutrition Is Measured and Analyzed

by Anastasia Marshak, Aishwarya Venkat, Helen Young and Elena N. Naumova

Int. J. Environ. Res. Public Health 2021, 18(4), 1828; https://doi.org/10.3390/ijerph18041828 - 13 Feb 2021

Cited by 17 | Viewed by 3392

Abstract

Seasonality is a critical source of vulnerability across most human activities and natural processes, including the underlying and immediate drivers of acute malnutrition. However, while there is general agreement that acute malnutrition is highly variable within and across years, the evidence base is [...] Read more.

Seasonality is a critical source of vulnerability across most human activities and natural processes, including the underlying and immediate drivers of acute malnutrition. However, while there is general agreement that acute malnutrition is highly variable within and across years, the evidence base is limited, resulting in an overreliance on assumptions of seasonal peaks. We review the design and analysis of 24 studies exploring the seasonality of nutrition outcomes in Africa’s drylands, providing a summary of approaches and their advantages and disadvantages. Over half of the studies rely on two to four time points within the year and/or the inclusion of time as a categorical variable in the analysis. While such approaches simplify interpretation, they do not correspond to the climatic variability characteristic of drylands or the relationship between climatic variability and human activities. To better ground our understanding of the seasonality of acute malnutrition in a robust evidence base, we offer recommendations for study design and analysis, including drawing on participatory methods to identify community perceptions of seasonality, use of longitudinal data and panel analysis with approaches borrowed from the field of infectious diseases, and linking oscillations in nutrition data with climatic data. Full article

(This article belongs to the Special Issue Infectious Disease Modeling in the Era of Complex Data)

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