Infectious Disease Data Research Articles

P-1447. Does Race or Social Vulnerability Play a Role in Antimicrobial Utilization for Admitted Patients? A Retrospective Analysis in a Large Healthcare System

Abstract Background Rising antibiotic resistance is a major public health threat. In 2015 our organization responded by establishing antimicrobial stewardship programs in all acute care facilities. A review of the literature has shown concerning disparities in antibiotic use across different populations, potentially contributing to unequal health outcomes. Our organization has an expansive geographic footprint that encompasses diverse patient populations which precipitates the need to evaluate care across the system.Table 1.Antimicrobial Utilization by Race and SVIDOT - Days of Therapy per 1000 Days Present Methods Retrospective analysis of 103 hospitals across a large healthcare system of all admitted patients who received a target antibiotic between January 1, 2023 to December 31, 2023. The primary clinical outcome was antimicrobial utilization defined as duration of therapy (DOT) per 1000 days present for four antibiotic groups (total systemic, 3rd generation cephalosporins, broad-spectrum beta-lactams, vancomycin). Data were stratified across patient race and social vulnerability index (SVI) to identify factors that may be contributing to gaps in equitable care. Results A total of 665,312 patients were included in the analysis (70.5% White, 17.6% Black/African American (AA)). A greater proportion of White patients were 65 years and older compared with the Black/AA population which were primarily 18 to 64 years. Infectious disease diagnoses accounted for 14.6% of all encounters. Antibiotic utilization for White patients was 13% higher than Black/AA in Total Systemic antibiotics (p< 0.001), and persisted when examined for infectious and non-infectious disease diagnoses (5% and 11.3% higher in White patients, respectively). Minimal differences were found when stratified by SVI. Conclusion Large differences in antibiotic utilization were observed by race but were not as pronounced by SVI. Additional factors such as age, diagnosis, and regional variability may need to be further examined in future analyses. Although these disparities were observed, our internal mortality data for infectious diseases shows no differences by race. Additional investigation is warranted to determine appropriateness of antibiotic use in each population to further elucidate on the disparities identified in this study. Disclosures All Authors: No reported disclosures

Forecasting the trend of tuberculosis incidence in Anhui Province based on machine learning optimization algorithm, 2013–2023

Tuberculosis has been one of the most common communicable diseases raising global concerns. Accurately predicting the incidence of Tuberculosis remains challenging. Here we constructed a time-series analysis and fusion tool using multi-source data, and aimed to more accurately predict the incidence trend of tuberculosis of Anhui Province from 2013 to 2023. Random forest algorithm (RF), Feature Recursive Elimination (RFE) and Least absolute shrinkage and selection operator (LASSO) were implemented to improve the derivation of features related to infectious diseases and feature work. Based on the characteristics of infectious disease data, a model of RF-RFE-LASSO integrated particle swarm optimization multiple inputs long short term memory recurrent neural network (RRL-PSO-MiLSTM) was created to perform more accurate prediction. Results showed that the PSO-MiLSTM achieved excellent prediction results compared with common single-input and multi-input time-series models (test set MSE:42.3555, MAE: 59.3333, RMSE: 146.7237, MAPE: 2.1133, R2: 0.8634). PSO-MiLSTM enriches and complements the methodological research content of calibrating the time-series predictive analysis of infectious diseases using multi-source data, and can be used as a brand-new benchmark for the analysis of influencing factors and trend prediction of infectious diseases at the public health level in the future, as well as providing a reference for incidence rate prediction of infectious diseases.

Assessing the Impact of Frailty on Infection Risk in Older Adults: Prospective Observational Cohort Study.

Infectious diseases are among the leading causes of death and disability and are recognized as a major cause of health loss globally. At the same time, frailty as a geriatric syndrome is a rapidly growing major public health problem. However, few studies have investigated the incidence and risk of infectious diseases in frail older people. Thus, research on frailty and infectious diseases is urgently needed. The purpose of this study was to evaluate the association between frailty and infectious diseases among older adults aged 65 years and older. In this prospective observational cohort study, we have analyzed the infectious disease prevalence outcomes of older adults aged 65 years and older who participated in frailty epidemiological surveys from March 1, 2018, to March 2023 in Dalang Town, Dongguan City, and from March 1, 2020, to March 2023 in Guancheng Street, Dongguan City. This study has an annual on-site follow-up. Incidence data for infectious diseases were collected through the Chinese Disease Control and Prevention Information System-Infectious Disease Monitoring and Public Health Emergency Monitoring System. A project-developed frailty assessment scale was used to assess the frailty status of study participants. We compared the incidence rate ratios (IRR) of each disease across frailty status, age, and gender to determine the associations among frailty, gender, age, and infectious diseases. Cox proportional hazards regression was conducted to identify the effect of frailty on the risk of demographic factors and frailty on the risk of infectious diseases, with estimations of the hazard ratio and 95% CI. A total of 235 cases of 12 infectious diseases were reported during the study period, with an incidence of 906.21/100,000 person-years in the frailty group. In the same age group, the risk of infection was higher in men than women. Frail older adults had a hazard ratio for infectious diseases of 1.50 (95% CI 1.14-1.97) compared with healthy older adults. We obtained the same result after sensitivity analyses. For respiratory tract-transmitted diseases (IRR 1.97, 95% CI 1.44-2.71) and gastrointestinal tract-transmitted diseases (IRR 3.67, 95% CI 1.39-10.74), frail older adults are at risk. Whereas no significant association was found for blood-borne, sexually transmitted, and contact-transmitted diseases (IRR 0.76, 95% CI 0.37-1.45). Our study provides additional evidence that frailty components are significantly associated with infectious diseases. Health care professionals must pay more attention to frailty in infectious disease prevention and control.

Establishment and application of infectious disease monitoring, early warning and disposal system

Using big data and artificial intelligence to establish a multi-point monitoring, early warning, and disposal system to achieve early warning and intervention of infectious disease outbreaks is an important means of controlling the spread of the epidemic. Taking Xiaoshan district as an example, this study analyzes the monitoring contents, warning methods, and application effectiveness of the infectious disease monitoring, early warning and disposal system. Based on Xiaoshan's health big data resources, the system starts with syndrome, disease diagnosis and etiology. Through advanced technologies such as artificial intelligence and block chain, it realizes early identification of infectious disease outbreaks, data fusion, multi-cross collaboration, and closed-loop management. It has improved the sensitivity of clustered outbreaks monitoring and the effectiveness of epidemic disposal and provided a reference for grassroots disease prevention and control departments to establish an infectious disease monitoring and early warning system.

Evaluating information management system in epidemic infectious diseases in Iran

Health information management is a vital and constructive component of the health system, refers to the process of producing and collecting, organising and storing, analysing, disseminating and using information. The aim of this study was to evaluate the strengths and weaknesses of the information management system in epidemic infectious diseases in Iran, specifically focusing on the registration, reporting, quality, confidentiality, and security of infectious disease data. This assessment was conducted from the perspective of policymakers and experts responsible for data registration and reporting. After examining the processes of registering and reporting infectious disease data and interviewing experts, a researcher-designed questionnaire was prepared to evaluate the infectious disease information management system. To assess the content validity of the Content Validity Index and Content Validity Ratio Index, a questionnaire was utilized. The reliability of the questionnaire was confirmed using Cronbach's alpha. By employing purposeful sampling and adhering to the inclusion criteria, 150 participants were included in the study. Questionnaires were distributed via email, WhatsApp, or Telegram to employees at various levels of Iran's health and treatment systems who were responsible for registering and reporting infectious disease data. The study encompassed 100 participants who successfully concluded the research. The results highlight that the key strength of healthcare data registration lies in its ability to "depict the epidemic curve during outbreaks of infectious diseases." Conversely, a notable weakness was the "insufficient collaboration from non-academic sectors (e.g., clinics, private laboratories) in registering and reporting infectious diseases. The present study's findings suggest that the issue lies not in the framework itself, but rather in the execution and functionality of the strategies. We can cultivate a repository of reliable and beneficial data by incorporating initiatives like training programs, enforcing regulations with consequences for inadequate data documentation, offering both material and motivational rewards, and streamlining all data collection and reporting systems.

A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases

BackgroundDescribing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection.MethodsThis study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China.ResultsWhen the spatio-temporal variation was small (time delay coefficient: 0.1–0.2, spatial sparsity:0.1–0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2–0.3, spatial sparsity: 0.6–0.9).ConclusionThis study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators—the time delay coefficient and spatial sparsity—into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.

Pooled Cohort Profile: ReCoDID Consortium's Harmonized Acute Febrile Illness Arbovirus Meta-Cohort.

Infectious disease (ID) cohorts are key to advancing public health surveillance, public policies, and pandemic responses. Unfortunately, ID cohorts often lack funding to store and share clinical-epidemiological (CE) data and high-dimensional laboratory (HDL) data long term, which is evident when the link between these data elements is not kept up to date. This becomes particularly apparent when smaller cohorts fail to successfully address the initial scientific objectives due to limited case numbers, which also limits the potential to pool these studies to monitor long-term cross-disease interactions within and across populations. CE data from 9 arbovirus (arthropod-borne viruses) cohorts in Latin America were retrospectively harmonized using the Maelstrom Research methodology and standardized to Clinical Data Interchange Standards Consortium (CDISC). We created a harmonized and standardized meta-cohort that contains CE and HDL data from 9 arbovirus studies from Latin America. To facilitate advancements in cross-population inference and reuse of cohort data, the Reconciliation of Cohort Data for Infectious Diseases (ReCoDID) Consortium harmonized and standardized CE and HDL from 9 arbovirus cohorts into 1 meta-cohort. Interested parties will be able to access data dictionaries that include information on variables across the data sets via Bio Studies. After consultation with each cohort, linked harmonized and curated human cohort data (CE and HDL) will be made accessible through the European Genome-phenome Archive platform to data users after their requests are evaluated by the ReCoDID Data Access Committee. This meta-cohort can facilitate various joint research projects (eg, on immunological interactions between sequential flavivirus infections and for the evaluation of potential biomarkers for severe arboviral disease).

Factors associated with death in patients admitted with Ebola virus disease to Ebola Treatment Units in Guinea, Sierra Leone, and Liberia – December 2013 to March 2016

Background The 2013-2016 West African Ebola Virus Disease (EVD) outbreak resulted in 28,600 cases and 11,300 deaths officially reported to the World Health Organization. Previous studies investigating factors associated with death had conflicting findings, interventions showing promising outcomes had small sample sizes, studies were often single- or dual-country based and most focused on laboratory-confirmed EVD and not on clinically-suspected EVD. We used the Ebola data platform of the Infectious Disease Data Observatory (IDDO) to review individual patient records to assess factors associated with death, and particularly whether there were differences between laboratory-confirmed and clinically-suspected cases. Methods This was a cohort study involving analysis of secondary data in the IDDO database. The study population included all patients classified as having either clinically-suspected or laboratory-confirmed EVD, admitted to 22 Ebola Treatment Units (ETU) in Guinea, Liberia and Sierra Leone between December 2013 and March 2016. Baseline characteristics and treatments were documented along with ETU exit outcomes. Factors associated with death were investigated by multivariable modified Poisson regression. Results There were 14,163 patients, of whom 6,208 (43.8%) were laboratory-confirmed and 7,955 (56.2%) were clinically-suspected. Outcomes were not recorded in 2,889 (20.4%) patients. Of the 11,274 patients with known outcomes, 4,090 (36.3%) died: 2,956 (43.6%) with laboratory-confirmed EVD and 1,134 (18.8%) with clinically-suspected EVD. The strongest risk factor for death was confirmed disease status. Patients with laboratory-confirmed disease had 2.9 times higher risk of death compared to clinically-suspected patients, after adjusting for other co-variables. Other factors significantly associated with death included a higher risk for patients aged ≥60 years and a lower risk for patients in Sierra Leone. Conclusions Although laboratory-confirmed patients admitted to ETUs fared worse than clinically-suspected patients, the latter still had a substantial risk of death and more attention needs to be paid to this group in future EVD outbreaks.

Blood transfusion in the care of patients with visceral leishmaniasis: a review of practices in therapeutic efficacy studies.

Blood transfusion remains an important aspect of patient management in visceral leishmaniasis (VL). However, transfusion triggers considered are poorly understood. This review summarises the transfusion practices adopted in VL efficacy studies using the Infectious Diseases Data Observatory VL clinical trials library. Of the 160 studies (1980-2021) indexed in the IDDO VL library, description of blood transfusion was presented in 16 (10.0%) (n=3459 patients) studies. Transfusion was initiated solely based on haemoglobin (Hb) measurement in nine studies, combining Hb measurement with an additional condition (epistaxis/poor health/clinical instability) in three studies and the criteria was not mentioned in four studies. The Hb threshold range for triggering transfusion was 3-8 g/dL. The number of patients receiving transfusion was explicitly reported in 10 studies (2421 patients enrolled, 217 underwent transfusion). The median proportion of patients who received transfusion in a study was 8.0% (Interquartile range: 4.7% to 47.2%; range: 0-100%; n=10 studies). Of the 217 patients requiring transfusion, 58 occurred before VL treatment initiation, 46 during the treatment/follow-up phase and the time was not mentioned in 113. This review describes the variation in clinical practice and is an important initial step in policy/guideline development, where both the patient's Hb concentration and clinical status must be considered.

Epidemicity indices and reproduction numbers from infectious disease data in connected human populations

We focus on distinctive data-driven measures of the fate of ongoing epidemics. The relevance of our pursuit is suggested by recent results proving that the short-term temporal evolution of infection spread is described by an epidemicity index related to the maximum instantaneous growth rate of new infections, echoing concepts and tools developed to study the reactivity of ecosystems. Suitable epidemicity indices can showcase the dynamics of infections, together with commonly employed effective reproduction numbers, especially when the latter assume values less than 1. In particular, epidemicity evaluates the short-term reactivity to perturbations of a disease-free equilibrium. Here, we show that sufficient epidemicity thresholds to prevent transient epidemic outbreaks in a spatially connected setting can be estimated by generalizing existing analogues derived when spatial effects are neglected. We specifically account for the discrete nature, in both space and time, of surveillance data of the type typically employed to estimate effective reproduction numbers that formed the bulk of the communication of the state of the COVID-19 pandemic and its controls. After analyzing the effects of spatial heterogeneity on the considered prognostic indicators, we perform a short- and long-term analysis on the COVID-19 pandemic in Italy, showing that endemic conditions were maintained throughout the duration of our simulation despite stringent control measures. Our method provides a portfolio of prognostic indices that are essential to pinpoint the ongoing pandemic in both a qualitative and quantitative manner, as our results demonstrate. We base our conclusions on extended investigations of the effects of spatial fragmentation of communities of different sizes owing to connectivity by human mobility and contact scenarios, within real geographic contexts and synthetic setups designed to test our framework.

Dynamics of respiratory infectious diseases under rapid urbanization and COVID-19 pandemic in the subcenter of Beijing during 2014–2022

ObjectiveThe study analyzed the impact of urbanization on epidemiological characteristics of respiratory infectious disease in Tongzhou District, Beijing during 2014–2022 to provide reference for prevention and control priorities of respiratory infectious diseases during the innovative urbanization process in China. MethodsThe incidence data of notifiable respiratory infectious diseases (NRIDs) in Tongzhou Beijing during 2014–2022 were summarized. The trend of incidence rate was analyzed by Joinpoint regression model, and entropy method was performed to construct the comprehensive index of urbanization (CIU) and generalized linear model was used to analyze the influence of CIU on the incidence rate of respiratory infectious diseases. ResultsTotally 72616 NRIDs cases were reported in Tongzhou District during 2014–2022, and the incidence rate of NRIDs was higher during 2017–2019 (153/100 000) than during 2014–2016 (930/100 000) and during 2020–2022 (371/100 000), respectively (both P < 0.001). The CIU constantly increased with slight fluctuation in 2016 and 2018, respectively. The incidence rate of NRIDs showed an increase along with the CIU during 2014–2019 (r = 0.95, P = 0.004), while the incidence rate's tendency was interrupted by COVID-19 during 2020 with slight decrease in 2020–2021 and rebounded in 2022. For the patients aged <15 years, the incidence rate of NRIDs revealed a very sharp rise at the urbanization period without COVID-19 pandemic compared with that under pre-urbanization period (RR = 7.93, 95 % CI 7.63−8.24), and dropped off to the similar level as of pre-urbanization period when COVID-19 pandemic spread. ConclusionsUrbanization process may increase the incidence of NRIDs but constrained by COVID-19. Certain measures should be taken to prevent and control the effects by urbanization process, such as good natural environment with less population density, ecological environment with good air quality, promoted hand hygiene, mask wearing, keeping interpersonal distance, vaccination, media publicity for NRIDs’ prevention and control.

Ensemble learning methods of inference for spatially stratified infectious disease systems.

Individual level models are a class of mechanistic models that are widely used to infer infectious disease transmission dynamics. These models incorporate individual level covariate information accounting for population heterogeneity and are generally fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework. However, Bayesian MCMC methods of inference are computationally expensive for large data sets. This issue becomes more severe when applied to infectious disease data collected from spatially heterogeneous populations, as the number of covariates increases. In addition, summary statistics over the global population may not capture the true spatio-temporal dynamics of disease transmission. In this study we propose to use ensemble learning methods to predict epidemic generating models instead of time consuming Bayesian MCMC method. We apply these methods to infer disease transmission dynamics over spatially clustered populations, considering the clusters as natural strata instead of a global population. We compare the performance of two tree-based ensemble learning techniques: random forest and gradient boosting. These methods are applied to the 2001 foot-and-mouth disease epidemic in the U.K. and evaluated using simulated data from a clustered population. It is shown that the spatially clustered data can help to predict epidemic generating models more accurately than the global data.

The influence of job category (rank) in the occurrence of infectious diseases among police personnel in south eastern Nigeria from 2012 to 2021

The occurrence of infectious diseases among police officers is one of the neglected areas in health and scientific research. The aim of this study is to ascertain and assess the influence of job category (rank) in the occurrence of infectious diseases among police personnel in south eastern Nigeria from 2012 to 2021. The study was designed as a retrospective study using data from the central police clinics/hospitals for the period of ten years covering 2012 – 2021. The study population comprised all the recorded infectious disease data in the three police clinics/hospitals of study for police personnel documented from January 2011 to December 2021. Occurrences of infectious diseases were analysed through descriptive techniques such as construction of the frequency distribution and distribution charts. Chi-square test was performed to test for associating variables of interest in the study. The probability value (P) was used to interpret the significance level at 5% and P value less than 0.05 was considered statistically significant. The study found the overall rate of occurrence infectious diseases among police officers as 21.1% in the study area. Significant associating factors of infectious diseases occurrence among police personnel found in the study for job category is (P &lt; 0.0001, χ2=74.91, df = 1). Police officers at junior category were the most affected (24.1%) compared to the officers at senior category (11.9%). the rate of occurrence of infectious diseases is significant among junior category (rank) police personnel in the South Eastern Nigeria.

Daily Hospital Emerging Infectious Disease(EID) Workflow & Data Processing Automation.

Following the onset of the covid pandemic two years ago, the Ministry of Health(MOH)'s required all health care cluster groups to provide daily reporting of the emergency department as well as the inpatient situation in the respective healthcare institutions for oversight of the covid situation. In view of the improvements in the data availability and relief of the tedious manual collation, DAO was entrusted with the task to help enable and setup the standardized report and generation process moving forward.

Archetypal analysis of COVID-19 in Montana, USA, March 13, 2020 to April 26, 2022.

Infectious disease data can often involve complex spatial patterns intermixed with temporal trends. Archetypal Analysis is a method to mine complex spatio-temporal data, and can be used to discover the dynamics of spatial patterns. The application of Archetypal Analysis to epidemiological data is relatively new, and here we present one of the first applications on COVID-19 data from March 13, 2020 to April 26, 2022, for the counties of Montana, USA. We present three views of the data set decomposed with Archetypal Analysis. First, we evaluate the entire 56 county data set. Second, we use a mutual information calculation to remove counties whose dynamics are mainly independent from the other counties, reducing the set to 17 counties. Finally, we analyze the top ten counties in terms of population size to focus on the dynamics in the large cities in the state. For each data set, we analyze four significant disease outbreaks across Montana. Archetypal Analysis uncovers distinct spatial patterns for each outbreak and demonstrates that each has a unique trajectory across the state.

A comparative analysis of vaccine lists, prices, and candidates, and the national immunization program between China and the United States

Vaccines have an essential role in preventing infectious diseases and reducing the burden of disease. The differences in vaccine lists and prices, and the National Immunization Program (NIP) and vaccine development between China and the United States (US) in conjunction with epidemiologic data on infectious diseases were systematically compared. The epidemiologic data of infectious diseases in 2019 were extracted from the China National Health Commission and the US Centers for Disease Control and Prevention (CDC). The vaccine list was identified from the China National Medical Products Administration and US Food and Drug Administration databases. Vaccine prices were obtained via the China government procurement platform and the US CDC. The NIP vaccines for China and the US were obtained from the China and US CDCs. Vaccine candidates investigated in 2015-2022 were identified from the China Clinical Trial Registry Platform and the US clinicaltrials.gov database. Differences in the incidence of infectious diseases between China and the US were detected with both countries facing a lack of available vaccines for prevention of many diseases. The number of listed vaccines and preventable diseases in China was 59 and 36, respectively, which was higher than the US (45 and 31, respectively). The median price of NIP vaccines in China was significantly lower than the price in the US (median: $3.8 vs. $20; P&lt;0.001); however, there was no significant difference in the price of non-NIP vaccines (median: $68 vs. $86; P=0.498). Vaccines developed by local manufacturers were less expensive than imported products despite the absence of significant differences (median: $16 vs. $31; P=0.180). The number and types of NIP vaccines used to prevent infectious diseases in China were lower than the US. The majority of vaccine candidates in China were not novel compared to the US. Vaccines marketed in China for infectious diseases were comparable to the US. Our findings suggest that China should further expand NIP vaccines and incentive research and development on novel vaccines to improve accessibility based on infectious disease epidemiology.

The promise and performance of data ecosystems: Australia’s COVID-19 response

Ecosystem metaphors are increasingly used to describe complex business and civil society relationships. This article argues that the rise of ecosystem thinking in public policy and administration represents a new, radically decentred model of policy action. It focuses on ecosystems of data-driven modelling and experts who use government public health data. This study presents a conceptual framework that suggests effective data ecosystems undergo organic adaptation, display institutional leadership, and respond instrumentally to policymakers’ information needs. It examines how Australia’s infectious disease data ecosystem performed before and during the COVID-19 pandemic. In-depth interviews with data experts involved in infectious disease modelling in Australia revealed trade-offs within and between organic, institutional and instrumental dimensions, which COVID-19 exacerbated. The article concludes that data ecosystems represent a hybrid form of policymaking in practice and that tensions must be managed to maximise the contributions of data ecosystems to facilitate evidence-informed decision-making.

Forecasting the Spread of Viral Diseases in Jordan Using the SARIMA Statistical Model

Time series models can help predict disease. Incidence data can be used to predict future disease outbreaks. Advances in modeling techniques allow us to compare the predictive capabilities of different time series models. Public health monitoring systems give essential data for accurate forecasting of future epidemics. This paper describes a study that used two types of infectious disease data, namely Mumps and Chickenpox, collected from a Department of Statistics open source data in mainland Jordan, to assess the performance of time series methods, specifically Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors. The data collected from 2000 to 2023 were used as modeling and forecasting samples, respectively. The performance was evaluated using two metrics: mean absolute error and mean squared error. The statistical models' accuracy in predicting future epidemic illnesses established their use in epidemiological monitoring. The Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors model, which was used to estimate total mumps cases in Jordan, was applied to a real dataset over the years 2000 to 2023. The dataset was separated into three groups: 78% training, 9% validation, and 13% testing. The results showed a mean squared error of 26629 and a mean absolute error of 152. The model predicted that Jordan will have 2341 cases of mumps by 2028.

Iterative estimating equations for disease mapping with spatial zero‐inflated Poisson data

Spatial epidemiology often involves the analysis of spatial count data with an unusually high proportion of zero observations. While Bayesian hierarchical models perform very well for zero‐inflated data in many situations, a smooth response surface is usually required for the Bayesian methods to converge. However, for infectious disease data with excessive zeros, a Wombling issue with large spatial variation could make the Bayesian methods infeasible. To address this issue, we develop estimating equations associated with disease mapping by including over‐dispersion and spatial noises in a spatial zero‐inflated Poisson model. Asymptotic properties are derived for the parameter estimates. Simulations and data analysis are used to assess and illustrate the proposed method.

Modeling Infectious Disease Trend using Sobolev Polynomials

Trend analysis plays an important role in infectious disease control. An analysis of the underlying trend in the number of cases or the mortality of a particular disease allows one to characterize its growth. Trend analysis may also be used to evaluate the effectiveness of an intervention to control the spread of an infectious disease. However, trends are often not readily observable because of noise in data that is commonly caused by random factors, short-term repeated patterns, or measurement error. In this paper, a smoothing technique that generalizes the Whittaker-Henderson method to infinite dimension and whose solution is represented by a polynomial is applied to extract the underlying trend in infectious disease data. The solution is obtained by projecting the problem to a finite-dimensional space using an orthonormal Sobolev polynomial basis obtained from Gram-Schmidt orthogonalization procedure and a smoothing parameter computed using the Philippine Eagle Optimization Algorithm, which is more efficient and consistent than a hybrid model used in earlier work. Because the trend is represented by the polynomial solution, extreme points, concavity, and periods when infectious disease cases are increasing or decreasing can be easily determined. Moreover, one can easily generate forecast of cases using the polynomial solution. This approach is applied in the analysis of trends, and in forecasting cases of different infectious diseases.