Projected number of people with multiple sclerosis in Germany between 2015 and 2040.

BACKGROUND: The global prevalence of multiple sclerosis has shown a marked rise in recent decades, with Germany reporting the highest prevalence among European countries. This study aims to project the future number of people with multiple sclerosis in Germany until 2040 which is necessary for effective resource allocation and health care planning. METHODS: Based on data from the German statutory health insurance, the age- and sex-specific prevalence of multiple sclerosis was estimated applying mathematical relations between prevalence, incidence rate, and mortality rate. Subsequently, the projected prevalence was applied to the age structure of the German population between 2015 and 2040 to calculate the future number of people with multiple sclerosis. Several temporal trend scenarios pertaining to the incidence and mortality rate were compared. RESULTS: Application of current age-specific prevalence estimates combined with the projected population structure in 2040, results in a decline of 8% in the number of people with multiple sclerosis. More realistic scenarios that reflect on trends in mortality and incidence rates, project between 453,000 (+75%) and 477,000 (+85%) multiple sclerosis cases in 2040. It is expected that females will be affected nearly 2.5 times more frequently than males in 2040. CONCLUSION: The findings indicate a substantial rise in the prevalence of multiple sclerosis, ranging from 75% to 85% in 2040 compared to 2015. Assuming a constant age-specific prevalence between 2015 and 2040 without any temporal trends in mortality and incidence rates may underestimate the actual number of cases and consequently, future requirements for healthcare resources.

Feasibility of aligning creatine kinase MB activity and mass data in multicenter trials using generalized additive modeling.

OBJECTIVES: Elevated serum creatine kinase isoenzyme MB (CK-MB) levels indicate myocardial ischaemia and periprocedural myocardial injury during treatment of heart diseases. We established a method to predict CK-MB mass from activity data based on a prospective pilot study in order to simplify multicentre trials. METHODS: 38 elective cardiac surgery patients without acute myocardial ischaemia and terminal renal failure were recruited. CK-MB mass and activity were determined in venous blood samples drawn preoperatively, postoperatively, 6 h post-op, and 12 h post-op. Linear regression and generalized additive models (GAMs) were applied to describe the relationship of mass and activity. Influences of demographic and perioperative factors on the fit of GAMs was evaluated. The agreement of predicted and measured CK-MB masses was assessed by Bland-Altman analyses. RESULTS: Linear regression provided an acceptable overall fit (r2=0.834) but showed deviances at low CK-MB levels. GAMs did not benefit from the inclusion of age, body mass index, and surgical times. The minimal adequate model predicted CK-MB masses from activities, sex, and sampling time with an r2 of 0.981. Bland-Altman analyses confirmed narrow limits of agreement (spread: 8.87 µg/L) and the absence of fixed (p = 0.41) and proportional (p = 0.21) biases. CONCLUSIONS: GAM-based modelling of CK-MB data in a representative patient cohort allowed to predict CK-MB masses from activities, sex, and sampling time. This approach simplifies the integration of study centers with incompatible CK-MB data into multicentre trials in order to facilitate inclusion of CK-MB levels in statistical models.

The Arctic marine soundscape of the Amundsen Gulf, Western Canadian Arctic.

The underwater soundscape, a habitat component for Arctic marine mammals, is shifting. We examined the drivers of the underwater soundscape at three sites in the Amundsen Gulf, Northwest Territories, Canada from 2018 to 2019 and estimated the contribution of abiotic and biotic sources between 20 Hz and 24 kHz. Higher wind speeds and the presence of bearded seal (Erignathus barbatus) vocalizations led to increased SPL (0.41 dB/km/h and 3.87 dB, respectively), while higher ice concentration and air temperature led to decreased SPL (-0.39 dB/% and - 0.096 dB/°C, respectively). Other marine mammals did not significantly impact the ambient soundscape. The presence of vessel traffic led to increased SPLs (12.37 dB) but was quieter at distances farther from the recorder (-2.57 dB/log m). The presence of high frequency and broadband signals produced by ice led to increased SPLs (7.60 dB and 10.16 dB, respectively).

Computational Lower Limb Simulator Boundary Conditions to Reproduce Measured TKA Loading in a Cohort of Telemetric Implant Patients.

Recent advancements in computational modeling offer opportunities to refine total knee arthroplasty (TKA) design and treatment strategies. This study developed patient-specific simulator external boundary conditions (EBCs) using a PID-controlled lower limb finite element (FE) model. Calibration of the external actuation required to achieve measured patient-specific joint loading and motion was completed for nine patients with telemetric implants during gait, stair descent, and deep knee bend. The study also compared two EBC scenarios: activity-specific hip AP motion and pelvic rotation (that was averaged across all patients for an activity) and patient-specific hip AP motion and pelvic rotation. Including patient-specific data significantly improved reproduction of joint-level loading, reducing root mean squared error between the target and achieved loading by 28.7% and highlighting the importance of detailed patient data in replicating joint kinematics and kinetics. The principal component analysis (PCA) of the EBCs for the patient dataset showed that one component represented 77.8% of the overall variation, while the first three components represented 97.8%. Given the significant loading variability within the patient cohort, this group of patient-specific models can be run individually to provide insight into expected TKA mechanics variability, and the PCA can be utilized to further create reasonable EBCs that expand the variability evaluated.

A systematic review of the use of in-vehicle telematics in monitoring driving behaviours.

BACKGROUND: Road traffic deaths are increasing globally, and preventable driving behaviours are a significant cause of these deaths. In-vehicle telematics has been seen as technology that can improve driving behaviour. The technology has been adopted by many insurance companies to track the behaviours of their consumers. This systematic review presents a summary of the ways that in-vehicle telematics has been modelled and analysed. METHODOLOGY: Electronic searches were conducted on Scopus and Web of Science. Studies were only included if they had a sample size of 10 or more participants, collected their data over at least multiple days, and were published during or after 2010. 45 relevant papers were included in the review. 27 of these articles received a rating of "good" in the quality assessment. RESULTS: We found a divide in the literature regarding the use of in-vehicle telematics. Some articles were interested in the utility of in-vehicle telematics for insurance purposes, while others were interested in determining the influence that in-vehicle telematics has on driving behaviour. Machine learning analyses were the most common forms of analysis seen throughout the review, being especially common in articles with insurance-based outcomes. Acceleration, braking, and speed were the most common variables identified in the review. CONCLUSION: We recommend that future studies provide the demographical information of their sample so that the influence of in-vehicle telematics on the driving behaviours of different groups can be understood. It is also recommended that future studies use multi-level models to account for the hierarchical structure of the telematics data. This hierarchical structure refers to the individual trips for each driver.

Secondary multilevel mixed-effects modelling of seroprevalence trends of Crimean-Congo haemorrhagic fever.

Background: Some review papers and meta-analyses have investigated seroprevalence and fatality trends of the Crimean-Congo hemorrhagic fever (CCHF), but it is not clear if its seroprevalence is increasing. Aim: To investigate the trend in the seroprevalence of CCHF. Methods: We conducted a secondary analysis of the results of a meta-analysis of the seroprevalence of CCHF published in 2019. We used a multilevel mixed effects Poisson regression to find the predictors of seropositivity. To explain the magnitude effect, we reported an incidence rate ratio (IRR) with a 95% confidence interval (CI). We conducted multilevel modeling using Stata 14 for data analysis. Results: In the fixed effects model, time was significantly associated with increased seropositivity (IRR = 1.025, 95% CI = 1.021-1.030), and no significant association was found for local sampling (IRR = 1.026, 95% CI = 0.988-1.065). In the mixed effects model, random intercepts of the country and parallel of latitude were applied as 3 levels of the model (prevalence rate of each study, nested within countries and latitude parallel). Accordingly, time was significantly associated with a reduction of seropositivity (IRR = 0.899, 95% CI = 0.891-0.907), and local sampling was significantly associated with increased seropositivity (IRR = 2.477, 95% CI = 2.316-2.649). Conclusion: Despite reporting increasing trends for seroprevalence of CCHF in previous reviews and the fixed effects model of the present study, the secondary mixed effects modeling showed a decreasing trend. The multilevel generalized model is recommended for such temporal and spatial designs in the future.

The risk of mpox importation and subsequent outbreak potential in Chinese mainland: a retrospective statistical modelling study.

BACKGROUND: The 2022-2023 mpox (monkeypox) outbreak has spread rapidly across multiple countries in the non-endemic region, mainly among men who have sex with men (MSM). In this study, we aimed to evaluate mpox's importation risk, border screening effectiveness and the risk of local outbreak in Chinese mainland. METHODS: We estimated the risk of mpox importation in Chinese mainland from April 14 to September 11, 2022 using the number of reported mpox cases during this multi-country outbreak from Global.health and the international air-travel data from Official Aviation Guide. We constructed a probabilistic model to simulate the effectiveness of a border screening scenario during the mpox outbreak and a hypothetical scenario with less stringent quarantine requirement. And we further evaluated the mpox outbreak potential given that undetected mpox infections were introduced into men who have sex with men, considering different transmissibility, population immunity and population activity. RESULTS: We found that the reduced international air-travel volume and stringent border entry policy decreased about 94% and 69% mpox importations respectively. Under the quarantine policy, 15-19% of imported infections would remain undetected. Once a case of mpox is introduced into active MSM population with almost no population immunity, the risk of triggering local transmission is estimated at 42%, and would rise to > 95% with over six cases. CONCLUSIONS: Our study demonstrates that the reduced international air-travel volume and stringent border entry policy during the COVID-19 pandemic reduced mpox importations prominently. However, the risk could be substantially higher with the recovery of air-travel volume to pre-pandemic level. Mpox could emerge as a public health threat for Chinese mainland given its large MSM community.

Readmission Risk Based on Debility and Psychosocial Measures: The Western 9 Algorithm.

Many studies have stressed the importance of psychosocial determinants of health such as food insecurity, housing instability, and education level, as important drivers of health outcomes. Western Health developed a set of nine screening questions (the Western 9) based on psychosocial measures to profile patients enrolled in the HealthLinks Chronic Care initiative. An aggregate score was then converted into a Low, Medium and High risk profiles. The aim of the study was to see if the Western 9 questions added additional discriminative power to existing risk of readmission algorithms. Results show that the inclusion of the risk profiles significantly improved model fit and calibration compared to a baseline risk model. Suggestions for further refinement include developing weighted indices for the Western 9 to improve model fit.

Antibody selection strategies and their impact in predicting clinical malaria based on multi-sera data.

BACKGROUND: Nowadays, the chance of discovering the best antibody candidates for predicting clinical malaria has notably increased due to the availability of multi-sera data. The analysis of these data is typically divided into a feature selection phase followed by a predictive one where several models are constructed for predicting the outcome of interest. A key question in the analysis is to determine which antibodies  should be included in the predictive stage and whether they should be included in the original or a transformed scale (i.e. binary/dichotomized). METHODS: To answer this question, we developed three approaches for antibody selection in the context of predicting clinical malaria: (i) a basic and simple approach based on selecting antibodies via the nonparametric Mann-Whitney-Wilcoxon test; (ii) an optimal dychotomizationdichotomization approach where each antibody was selected according to the optimal cut-off via maximization of the chi-squared (χ2) statistic for two-way tables; (iii) a hybrid parametric/non-parametric approach that integrates Box-Cox transformation followed by a t-test, together with the use of finite mixture models and the Mann-Whitney-Wilcoxon test as a last resort. We illustrated the application of these three approaches with published serological data of 36 Plasmodium falciparum antigens for predicting clinical malaria in 121 Kenyan children. The predictive analysis was based on a Super Learner where predictions from multiple classifiers including the Random Forest were pooled together. RESULTS: Our results led to almost similar areas under the Receiver Operating Characteristic curves of 0.72 (95% CI = [0.62, 0.82]), 0.80 (95% CI = [0.71, 0.89]), 0.79 (95% CI = [0.7, 0.88]) for the simple, dichotomization and hybrid approaches, respectively. These approaches were based on 6, 20, and 16 antibodies, respectively. CONCLUSIONS: The three feature selection strategies provided a better predictive performance of the outcome when compared to the previous results relying on Random Forest including all the 36 antibodies (AUC = 0.68, 95% CI = [0.57;0.79]). Given the similar predictive performance, we recommended that the three strategies should be used in conjunction in the same data set and selected according to their complexity.

Survival with primary lung cancer in Northern Ireland: 1991-1992.

Lung cancer is a major cause of death in Western countries, but survival had never been studied in Northern Ireland (NI) on a population basis prior to this study. AIMS: The primary aims were to describe the survival of patients with primary lung cancer, evaluate the effect of treatment, identify patient characteristics influencing survival and treatment and describe current trends in survival. METHODS: A population-based study identified all incident cases of primary lung cancer in NI during 1991-2 and followed them for 21 months. Their clinical notes were traced and relevant details abstracted. Survival status was monitored via the Registrar General's Office, and ascertainment is thought to be near-complete. Appropriate statistical methods were used to analyse the survival data. RESULTS: Some 855 incident cases were studied. Their 1-year survival was 24.5% with a median survival time of 4.7 months. Surgical patients had the best 1-year survival, 76.8%; however, adjustment suggested that about half of the benefit could be attributed to case-mix factors. Factors influencing treatment allocation were also identified, and a screening test showed the discordance between 'model' and 'medic': 210 patients were misclassified. Finally, the current trend in 1-year survival observed in the Republic of Ireland was best in the British Isles. CONCLUSIONS: Overall, survival remains poor. The better survival of surgical patients is due, in part, to their superior case-mix profiles. Survival with other therapies is less good suggesting that the criteria for treatment might be relaxed with advantage using a treatment model to aid decision-making.

Effects of the Mixing Process on the Rheological Properties of Waste PET-Modified Bitumen.

This paper analyses the key findings of a study devoted to PET-modified bitumen. The research program was run according to the D-optimal experimental plan based on a factorial design. Five factors, i.e., the type of polymer (source), the type of bitumen (qualitative factors), PET amount, mixing rate, and mixing temperature (quantitative factors), controlled the bitumen-polymer mixing process. The experiment included a series of determinations of bitumen's rheological characteristics obtained by MSCR (Jnr, R) and G*/sin(δ) at 50 °C, 60 °C, and 70 °C. The low-temperature properties of the composite (critical temperature) were evaluated using a BBR test. The findings showed that bitumen modification with PET primarily reduced the creep susceptibility of the bituminous-polymer mixture. The low-temperature characteristics of the modified bitumen played a secondary but essential role. The amount of polymer and the mixing rate interacted with the temperature, significantly reducing the stiffness of the composite, while the type and amount of bitumen had a substantial effect on the results obtained in the BBR test. It is worth noting that when combining bitumen and plastomer, special attention should be paid to ensuring a high level of homogeneity of the mixture by controlling the parameters of the mixing process accordingly. The tests and analyses provided crucial models (GLM), which allowed for the prediction of the plastomer-modified bitumen's low- and high-temperature properties. The resulting relationships between factors and the identification of their impact on the bitumen properties enable a better understanding of the process of bitumen modification with PET. The conclusions presented here serve as a basis for future optimisation of the modified bitumen composition. The performed studies indicate that the use of >3% plastomer in bitumen 70/100 allows for a reduction in its susceptibility (MSCR) to below 0.5 kPa-1, making it suitable for bituminous mixtures for high-traffic roads. No significant increase in critical temperature (BBR) was observed.

Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models.

The evident shedding of the SARS-CoV-2 RNA particles from infected individuals into the wastewater opened up a tantalizing array of possibilities for prediction of COVID-19 prevalence prior to symptomatic case identification through community testing. Many countries have therefore explored the use of wastewater metrics as a surveillance tool, replacing traditional direct measurement of prevalence with cost-effective approaches based on SARS-CoV-2 RNA concentrations in wastewater samples. Two important aspects in building prediction models are: time over which the prediction occurs and space for which the predicted case numbers is shown. In this review, our main focus was on finding mathematical models which take into the account both the time-varying and spatial nature of wastewater-based metrics into account. We used six main characteristics as our assessment criteria: i) modelling approach; ii) temporal coverage; iii) spatial coverage; iv) sample size; v) wastewater sampling method; and vi) covariates included in the modelling. The majority of studies in the early phases of the pandemic recognized the temporal association of SARS-CoV-2 RNA concentration level in wastewater with the number of COVID-19 cases, ignoring their spatial context. We examined 15 studies up to April 2023, focusing on models considering both temporal and spatial aspects of wastewater metrics. Most early studies correlated temporal SARS-CoV-2 RNA levels with COVID-19 cases but overlooked spatial factors. Linear regression and SEIR models were commonly used (n = 10, 66.6 % of studies), along with machine learning (n = 1, 6.6 %) and Bayesian approaches (n = 1, 6.6 %) in some cases. Three studies employed spatio-temporal modelling approach (n = 3, 20.0 %). We conclude that the development, validation and calibration of further spatio-temporally explicit models should be done in parallel with the advancement of wastewater metrics before the potential of wastewater as a surveillance tool can be fully realised.

Prognostic models: What the statistician wants the clinician to know.

Prognostic model building is a process that begins much earlier than data analysis and ends later than when a model is reached. It requires careful delineation of a clinical question, methodical planning of the approach and attentive exploration of the data before attempting model building. Once following these important initial steps, the researcher may postulate a model to describe the process of interest and build such model. Once built, the model will need to be checked, validated and the exercise may take the researcher back a few steps - for instance, to adapt the model to fit a variable that displays a 'curved' pattern - to then return to check and validate the model again. To interpret and report the results it is vital to relate the output to the original question, to be transparent in the methodology followed and to understand the limitations of the data and the approach.

Parametric and non-parametric Poisson regression for modelling of the arterial input function in positron emission tomography.

Full quantification of Positron Emission Tomography (PET) requires an arterial input function (AIF) for measurement of certain targets, or using particular radiotracers, or for the quantification of specific outcome measures. The AIF represents the measurement of radiotracer concentrations in the arterial blood plasma over the course of the PET examination. Measurement of the AIF is prone to error as it is a composite measure created from the combination of multiple measurements of different samples with different equipment, each of which can be sources of measurement error. Moreover, its measurement requires a high degree of temporal granularity for early time points, which necessitates a compromise between quality and quantity of recorded samples. For these reasons, it is often desirable to fit models to this data in order to improve its quality before using it for quantification of radiotracer binding in the tissue. The raw observations of radioactivity in arterial blood and plasma samples are derived from radioactive decay, which is measured as a number of recorded counts. Count data have several specific properties, including the fact that they cannot be negative as well as a particular mean-variance relationship. Poisson regression is the most principled modelling strategy for working with count data, as it both incorporates and exploits these properties. However, no previous studies to our knowledge have taken this approach, despite the advantages of greater efficiency and accuracy which result from using the appropriate distributional assumptions. Here, we implement a Poisson regression modelling approach for the AIF as proof-of-concept of its application. We applied both parametric and non-parametric models for the input function curve. We show that a negative binomial distribution is a more appropriate error distribution for handling overdispersion. Furthermore, we extend this approach to a hierarchical non-parametric model which is shown to be highly resilient to missing data. We thus demonstrate that Poisson regression is both feasible and effective when applied to AIF data, and propose that this is a promising strategy for modelling blood count data for PET in future.

Agency effects on the binding of event elements in episodic memory.

Representing events in episodic memory in a coherent manner requires that their constituent elements are bound together. So far, only few moderators of these binding processes have been identified. Here we investigate whether the presence of an agentic element in an event facilitates binding. The results from six experiments provided no evidence for a facilitating effect of agency on the binding of event elements. In addition, binding effects were only found when event elements were presented simultaneously, but not when they were presented sequentially pairwise, contrary to previous findings. The results suggest that the presence of an agentic element in an event does not, or only to a very limited extent, contribute to the formation of coherent memory representations and that additional processes may be required when binding event elements across temporarily divided encoding episodes. These findings add to a growing body of research regarding moderators and processes relevant for the binding of event elements in episodic memory. Explanations of these findings and directions for future research are discussed.

They might all be marauders.

Circle theory predicts most offenders will reside within the smallest circle that encloses a pattern of offense locations, which defines the offender's crime range. Those who reside within the crime range reflect a marauder decision pattern, with the remainder reflecting a commuter decision pattern. Random simulations of marauders were conducted to determine the failure rate of the smallest circle to correctly identify a marauder decision pattern. Failure rates were higher as the number of offenses was reduced, but were systematic and predictable. Moreover, the published rates of the marauder vs commuter patterns correspond well with the predicted failure rate. A method for estimating the true size of an offender's criminal territory is presented and it is suggested that the vast majority of offenders are likely to be adopting a marauder decision pattern, and that true commuters may be far less than indicated by the circle test.

Selective adsorption of organic dyes from aqueous environment using fermented maize extract-enhanced graphene oxide-durian shell derived activated carbon composite.

A secure aquatic environment is essential for both aquatic and terrestrial life. However, rising populations and the industrial revolution have had a significant impact on the quality of the water environment. Despite the implementation of strong and adapted environmental policies for water treatment worldwide, the issue of organic dyes in wastewater remains challenging. Thus, this study aimed to develop an efficient, cost-effective, and sustainable material to treat methylene blue (MB) in an aqueous environment. In this research, maize extract solution (MES) was utilized as a green cross-linker to induce precipitation, conjugation, and enhance the adsorption performance of graphene oxide (GO) cross-linked with durian shell activated carbon (DSAC), resulting in the formation of a GO@DSAC composite. The composite was investigated for its adsorptive performance toward MB in aqueous media. The physicochemical characterization demonstrated that the cross-linking method significantly influenced the porous structure and surface chemistry of GO@DSAC. BET analysis revealed that the GO@DSAC exhibited dominant mesopores with a surface area of 803.67 m2/g. EDX and XPS measurements confirmed the successful cross-linking of GO with DSAC. The adsorption experiments were well described by the Harkin-Jura model and they followed pseudo-second order kinetics. The maximum adsorption capacity reached 666.67 mg/g at 318 K. Thermodynamic evaluation indicated a spontaneous, feasible, and endothermic in nature. Regenerability and reusability investigations demonstrated that the GO@DSAC composite could be reused for up to 10 desorption-adsorption cycles with a removal efficiency of 81.78%. The selective adsorptive performance of GO@DSAC was examined in a binary system containing Rhodamine B (RhB) and methylene orange (MO). The results showed a separation efficiency (α) of 98.89% for MB/MO and 93.66% for MB/RhB mixtures, underscoring outstanding separation capabilities of the GO@DSAC composite. Overall, the GO@DSAC composite displayed promising potential for the effective removal of cationic dyes from wastewater.

Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review.

BACKGROUND AND OBJECTIVES: When developing a clinical prediction model, assuming a linear relationship between the continuous predictors and outcome is not recommended. Incorrect specification of the functional form of continuous predictors could reduce predictive accuracy. We examine how continuous predictors are handled in studies developing a clinical prediction model. METHODS: We searched PubMed for clinical prediction model studies developing a logistic regression model for a binary outcome, published between July 01, 2020, and July 30, 2020. RESULTS: In total, 118 studies were included in the review (18 studies (15%) assessed the linearity assumption or used methods to handle nonlinearity, and 100 studies (85%) did not). Transformation and splines were commonly used to handle nonlinearity, used in 7 (n = 7/18, 39%) and 6 (n = 6/18, 33%) studies, respectively. Categorization was most often used method to handle continuous predictors (n = 67/118, 56.8%) where most studies used dichotomization (n = 40/67, 60%). Only ten models included nonlinear terms in the final model (n = 10/18, 56%). CONCLUSION: Though widely recommended not to categorize continuous predictors or assume a linear relationship between outcome and continuous predictors, most studies categorize continuous predictors, few studies assess the linearity assumption, and even fewer use methodology to account for nonlinearity. Methodological guidance is provided to guide researchers on how to handle continuous predictors when developing a clinical prediction model.

Modeling vaccination coverage during the 2022 central Ohio measles outbreak: a cross-sectional study.

Background: Of the eight large (>50 cases) US postelimination outbreaks, the first and last occurred in Ohio. Ohio's vaccination registry is incomplete. Community-level immunity gaps threaten more than two decades of measles elimination in the US. We developed a statistical model, VaxEstim, to rapidly estimate the early-phase vaccination coverage and immunity gap in the exposed population during the 2022 Central Ohio outbreak. Methods: We used reconstructed daily incidence (from publicly available data) and assumptions about the distribution of the serial interval, or the time between symptom onset in successive measles cases, to estimate the effective reproduction number (i.e., the average number of secondary infections caused by an infected individual in a partially immune population). We estimated early-phase measles vaccination coverage by comparing the effective reproduction number to the basic reproduction number (i.e., the average number of secondary infections caused by an infected individual in a fully susceptible population) while accounting for vaccine effectiveness. Finally, we estimated the early-phase immunity gap as the difference between the estimated critical vaccination threshold and vaccination coverage. Findings: VaxEstim estimated the early-phase vaccination coverage as 53% (95% credible interval, 21%-77%), the critical vaccination threshold as 93%, and the immunity gap as 42% (95% credible interval, 18%-74%). Interpretation: This study estimates a significant immunity gap in the exposed population during the early phase of the 2022 Central Ohio measles outbreak, suggesting a robust public health response is needed to identify the susceptible community and develop community-specific strategies to close the immunity gap. Funding: This work was supported in part by the National Institute of General Medical Sciences, National Institutes of Health; the UK Medical Research Council (MRC); the Foreign, Commonwealth and Development Office; the National Institute for Health Research (NIHR) Health Protection Research Unit in Modelling Methodology; Imperial College London, and the London School of Hygiene & Tropical Medicine, Community Jameel; the EDCTP2 programme, supported by the EU; and the Sergei Brin Foundation.