Directory

Abstract: OBJECTIVE: Explorative analysis of the effects of vaccination policy on measles incidence. DESIGN: Retrospective study and mathematical modelling. METHOD: Analysis of national and regional case notifications of measles provided by the Inspectorate of Health in the Netherlands over the period from January 1976 (when vaccination was started) through September 1999. Also computer simulations with a mathematical epidemic model of measles were used to calculate the incidence of measles from 1976 onwards. RESULTS: According to the model results, measles should not persist with the current vaccination programme. However, the case notification data showed that measles appeared to persist at a nation-wide level. At a regional level, measles did not persist, not even in regions with low vaccine coverage. A possible cause of the unexpected persistence at the national level is the asynchronous regional course of the 6-year epidemic cycle of measles, where measles infection 'jumps' from one region to the other.

Abstract: BACKGROUND: Dynamic models of infection transmission can project future disease burden within a population. Few dynamic measles models have been developed for low-income countries, where measles disease burden is highest. Our objective was to review the literature on measles epidemiology in low-income countries, with a particular focus on data that are needed to parameterize dynamic models. METHODS: We included age-stratified case reporting and seroprevalence studies with fair to good sample sizes for mostly urban African and Indian populations. We emphasized studies conducted before widespread immunization. We summarized age-stratified attack rates and seroprevalence profiles across these populations. Using the study data, we fitted a "representative" seroprevalence profile for African and Indian settings. We also used a catalytic model to estimate the age-dependent force of infection for individual African and Indian studies where seroprevalence was surveyed. We used these data to quantify the effects of population density on the basic reproductive number R0. RESULTS: The peak attack rate usually occurred at age 1 year in Africa, and 1 to 2 years in India, which is earlier than in developed countries before mass vaccination. Approximately 60% of children were seropositive for measles antibody by age 2 in Africa and India, according to the representative seroprevalence profiles. A statistically significant decline in the force of infection with age was found in 4 of 6 Indian seroprevalence studies, but not in 2 African studies. This implies that the classic threshold result describing the critical proportion immune (pc) required to eradicate an infectious disease, pc = 1-1/R0, may overestimate the required proportion immune to eradicate measles in some developing country populations. A possible, though not statistically significant, positive relation between population density and R0 for various Indian and African populations was also found. These populations also showed a similar pattern of waning of maternal antibodies. Attack rates in rural Indian populations show little dependence on vaccine coverage or population density compared to urban Indian populations. Estimated R0 values varied widely across populations which has further implications for measles elimination. CONCLUSIONS: It is possible to develop a broadly informative dynamic model of measles transmission in low-income country settings based on existing literature, though it may be difficult to develop a model that is closely tailored to any given country. Greater efforts to collect data specific to low-income countries would aid in control efforts by allowing highly population-specific models to be developed.

Abstract: The growing demand for spatially detailed data to advance the Sustainable Development Goals agenda of 'leaving no one behind' has resulted in a shift in focus from aggregate national and province-based metrics to small areas and high-resolution grids in the health and development arena. Vaccination coverage is customarily measured through aggregate-level statistics, which mask fine-scale heterogeneities and 'coldspots' of low coverage. This paper develops a methodology for high-resolution mapping of vaccination coverage using areal data in settings where point-referenced survey data are inaccessible. The proposed methodology is a binomial spatial regression model with a logit link and a combination of covariate data and random effects modelling two levels of spatial autocorrelation in the linear predictor. The principal aspect of the model is the melding of the misaligned areal data and the prediction grid points using the regression component and each of the conditional autoregressive and the Gaussian spatial process random effects. The Bayesian model is fitted using the INLA-SPDE approach. We demonstrate the predictive ability of the model using simulated data sets. The results obtained indicate a good predictive performance by the model, with correlations of between 0.66 and 0.98 obtained at the grid level between true and predicted values. The methodology is applied to predicting the coverage of measles and diphtheria-tetanus-pertussis vaccinations at 5 × 5 km(2) in Afghanistan and Pakistan using subnational Demographic and Health Surveys data. The predicted maps are used to highlight vaccination coldspots and assess progress towards coverage targets to facilitate the implementation of more geographically precise interventions. The proposed methodology can be readily applied to wider disaggregation problems in related contexts, including mapping other health and development indicators.

Abstract: Achieving equity in vaccination coverage has been a critical priority within the global health community. Despite increased efforts recently, certain populations still have a high proportion of un- and under-vaccinated children in many low- and middle-income countries (LMICs). These populations are often assumed to reside in remote-rural areas, urban slums and conflict-affected areas. Here, we investigate the effects of these key community-level factors, alongside a wide range of other individual, household and community level factors, on vaccination coverage. Using geospatial datasets, including cross-sectional data from the most recent Demographic and Health Surveys conducted between 2008 and 2018 in nine LMICs, we fitted Bayesian multi-level binary logistic regression models to determine key community-level and other factors significantly associated with non- and under-vaccination. We analyzed the odds of receipt of the first doses of diphtheria-tetanus-pertussis (DTP1) vaccine and measles-containing vaccine (MCV1), and receipt of all three recommended DTP doses (DTP3) independently, in children aged 12-23 months. In bivariate analyses, we found that remoteness increased the odds of non- and under-vaccination in nearly all the study countries. We also found evidence that living in conflict and urban slum areas reduced the odds of vaccination, but not in most cases as expected. However, the odds of vaccination were more likely to be lower in urban slums than formal urban areas. Our multivariate analyses revealed that the key community variables-remoteness, conflict and urban slum-were sometimes associated with non- and under-vaccination, but they were not frequently predictors of these outcomes after controlling for other factors. Individual and household factors such as maternal utilization of health services, maternal education and ethnicity, were more common predictors of vaccination. Reaching the Immunisation Agenda 2030 target of reducing the number of zero-dose children by 50% by 2030 will require country tailored analyses and strategies to identify and reach missed communities with reliable immunisation services.

Abstract: Background In 2008 all WHO member states endorsed a target of 90% reduction in measles mortality by 2010 over 2000 levels. We developed a model to estimate progress made towards this goal. Methods We constructed a state-space model with population and immunisation coverage estimates and reported surveillance data to estimate annual national measles cases, distributed across age classes. We estimated deaths by applying age-specific and country-specific case-fatality ratios to estimated cases in each age-country class. Findings Estimated global measles mortality decreased 74% from 535 300 deaths (95% CI 347 200-976 400) in 2000 to 139 300 (71 200-447 800) in 2010. Measles mortality was reduced by more than three-quarters in all WHO regions except the WHO southeast Asia region. India accounted for 47% of estimated measles mortality in 2010, and the WHO African region accounted for 36%. Interpretation Despite rapid progress in measles control from 2000 to 2007, delayed implementation of accelerated disease control in India and continued outbreaks in Africa stalled momentum towards the 2010 global measles mortality reduction goal. Intensified control measures and renewed political and financial commitment are needed to achieve mortality reduction targets and lay the foundation for future global eradication of measles.

Abstract:

Abstract: Despite vaccination being one of the most effective public health interventions, there are persisting inequalities and inequities in immunisation. Understanding the differences in subnational vaccine impact can help improve delivery mechanisms and policy. We analyse subnational vaccination coverage of measles first-dose (MCV1) and estimate patterns of inequalities in impact, represented as deaths averted, across 45 countries in Africa. We also evaluate how much this impact would improve under more equitable vaccination coverage scenarios. Using coverage data for MCV1 from 2000-2019, we estimate the number of deaths averted at the first administrative level. We use the ratio of deaths averted per vaccination from two mathematical models to extrapolate the impact at a subnational level. Next, we calculate inequality for each country, measuring the spread of deaths averted across its regions, accounting for differences in population. Finally, using three more equitable vaccination coverage scenarios, we evaluate how much impact of MCV1 immunisation could improve by (1) assuming all regions in a country have at least national coverage, (2) assuming all regions have the observed maximum coverage; and (3) assuming all regions have at least 80% coverage. Our results show that progress in coverage and reducing inequality has slowed in the last decade in many African countries. Under the three scenarios, a significant number of additional deaths in children could be prevented each year; for example, under the observed maximum coverage scenario, global MCV1 coverage would improve from 76% to 90%, resulting in a further 363(95%CrI:299-482) deaths averted per 100,000 live births. This paper illustrates that estimates of the impact of MCV1 immunisation at a national level can mask subnational heterogeneity. We further show that a considerable number of deaths could be prevented by maximising equitable access in countries with high inequality when increasing the global coverage of MCV1 vaccination.

Abstract: BACKGROUND: The expansion of childhood vaccination programs in low and middle income countries has been a substantial public health success story. Indicators of the performance of intervention programmes such as coverage levels and numbers covered are typically measured through national statistics or at the scale of large regions due to survey design, administrative convenience or operational limitations. These mask heterogeneities and 'coldspots' of low coverage that may allow diseases to persist, even if overall coverage is high. Hence, to decrease inequities and accelerate progress towards disease elimination goals, fine-scale variation in coverage should be better characterized. METHODS: Using measles as an example, cluster-level Demographic and Health Surveys (DHS) data were used to map vaccination coverage at 1 km spatial resolution in Cambodia, Mozambique and Nigeria for varying age-group categories of children under five years, using Bayesian geostatistical techniques built on a suite of publicly available geospatial covariates and implemented via Markov Chain Monte Carlo (MCMC) methods. RESULTS: Measles vaccination coverage was found to be strongly predicted by just 4-5 covariates in geostatistical models, with remoteness consistently selected as a key variable. The output 1 × 1 km maps revealed significant heterogeneities within the three countries that were not captured using province-level summaries. Integration with population data showed that at the time of the surveys, few districts attained the 80% coverage, that is one component of the WHO Global Vaccine Action Plan 2020 targets. CONCLUSION: The elimination of vaccine-preventable diseases requires a strong evidence base to guide strategies and inform efficient use of limited resources. The approaches outlined here provide a route to moving beyond large area summaries of vaccination coverage that mask epidemiologically-important heterogeneities to detailed maps that capture subnational vulnerabilities. The output datasets are built on open data and methods, and in flexible format that can be aggregated to more operationally-relevant administrative unit levels.

Abstract: The safe, highly effective measles vaccine has been recommended globally since 1974, yet in 2017 there were more than 17 million cases of measles and 83,400 deaths in children under 5 years old, and more than 99% of both occurred in low- and middle-income countries (LMICs)(1-4). Globally comparable, annual, local estimates of routine first-dose measles-containing vaccine (MCV1) coverage are critical for understanding geographically precise immunity patterns, progress towards the targets of the Global Vaccine Action Plan (GVAP), and high-risk areas amid disruptions to vaccination programmes caused by coronavirus disease 2019 (COVID-19)(5-8). Here we generated annual estimates of routine childhood MCV1 coverage at 5 × 5-km(2) pixel and second administrative levels from 2000 to 2019 in 101 LMICs, quantified geographical inequality and assessed vaccination status by geographical remoteness. After widespread MCV1 gains from 2000 to 2010, coverage regressed in more than half of the districts between 2010 and 2019, leaving many LMICs far from the GVAP goal of 80% coverage in all districts by 2019. MCV1 coverage was lower in rural than in urban locations, although a larger proportion of unvaccinated children overall lived in urban locations; strategies to provide essential vaccination services should address both geographical contexts. These results provide a tool for decision-makers to strengthen routine MCV1 immunization programmes and provide equitable disease protection for all children.

Abstract: Geographically precise identification and targeting of populations at risk of vaccine-preventable diseases has gained renewed attention within the global health community over the last few years. District level estimates of vaccination coverage and corresponding zero-dose prevalence constitute a potentially useful evidence base to evaluate the performance of vaccination strategies. These estimates are also valuable for identifying missed communities, hence enabling targeted interventions and better resource allocation. Here, we fit Bayesian geostatistical models to map the routine coverage of the first doses of diphtheria-tetanus-pertussis vaccine (DTP1) and measles-containing vaccine (MCV1) and corresponding zero-dose estimates in Nigeria at 1x1 km resolution and the district level using geospatial data sets. We also map MCV1 coverage before and after the 2019 measles vaccination campaign in the northern states to further explore variations in routine vaccine coverage and to evaluate the effectiveness of both routine immunization (RI) and campaigns in reaching zero-dose children. Additionally, we map the spatial distributions of reported measles cases during 2018 to 2020 and explore their relationships with MCV zero-dose prevalence to highlight the public health implications of varying performance of vaccination strategies across the country. Our analysis revealed strong similarities between the spatial distributions of DTP and MCV zero dose prevalence, with districts with the highest prevalence concentrated mostly in the northwest and the northeast, but also in other areas such as Lagos state and the Federal Capital Territory. Although the 2019 campaign reduced MCV zero-dose prevalence substantially in the north, pockets of vulnerabilities remained in areas that had among the highest prevalence prior to the campaign. Importantly, we found strong correlations between measles case counts and MCV RI zero-dose estimates, which provides a strong indication that measles incidence in the country is mostly affected by RI coverage. Our analyses reveal an urgent and highly significant need to strengthen the country's RI program as a longer-term measure for disease control, whilst ensuring effective campaigns in the short term.

Abstract: BACKGROUND: Substantial inequalities exist in childhood vaccination coverage levels. To increase vaccine uptake, factors that predict vaccination coverage in children should be identified and addressed. METHODS: Using data from the 2018 Nigeria Demographic and Health Survey and geospatial data sets, we fitted Bayesian multilevel binomial and multinomial logistic regression models to analyse independent predictors of three vaccination outcomes: receipt of the first dose of Pentavalent vaccine (containing diphtheria-tetanus-pertussis, Hemophilus influenzae type B and Hepatitis B vaccines) (PENTA1) (n = 6059) and receipt of the third dose having received the first (PENTA3/1) (n = 3937) in children aged 12-23 months, and receipt of measles vaccine (MV) (n = 11839) among children aged 12-35 months. RESULTS: Factors associated with vaccination were broadly similar for documented versus recall evidence of vaccination. Based on any evidence of vaccination, we found that health card/document ownership, receipt of vitamin A and maternal educational level were significantly associated with each outcome. Although the coverage of each vaccine dose was higher in urban than rural areas, urban residence was not significant in multivariable analyses that included travel time. Indicators relating to socio-economic status, as well as ethnic group, skilled birth attendance, lower travel time to the nearest health facility and problems seeking health care were significantly associated with both PENTA1 and MV. Maternal religion was related to PENTA1 and PENTA3/1 and maternal age related to MV and PENTA3/1; other significant variables were associated with one outcome each. Substantial residual community level variances in different strata were observed in the fitted models for each outcome. CONCLUSION: Our analysis has highlighted socio-demographic and health care access factors that affect not only beginning but completing the vaccination series in Nigeria. Other factors not measured by the DHS such as health service quality and community attitudes should also be investigated and addressed to tackle inequities in coverage.

Abstract: INTRODUCTION: Achieving the ambitious goals of the Immunisation Agenda 2030 (IA2030) requires a deeper understanding of factors influencing under-vaccination, including timely vaccination. This study investigates the demand- and supply-side determinants influencing the timely uptake of key childhood vaccines scheduled throughout the first year of life in The Gambia. METHODS: We used two nationally-representative datasets: the 2019-20 Gambian Demographic and Health Survey and the 2019 national immunisation facility mapping. Using Bayesian multi-level binary logistic regression models, we identified key factors significantly associated with timely vaccination for five key vaccines: birth dose of hepatitis-B (HepB0), first, second, and third doses of the pentavalent vaccine (Penta1, Penta2, Penta3), and first-dose of measles-containing vaccine (MCV1) in children aged 12-35 months. We report the adjusted Odds Ratios (aORs) and 95 % Credible Intervals (95 % CIs) in each case. RESULTS: We found that demand-side factors, such as ethnicity, household wealth status, maternal education, maternal parity, and the duration of the household's residency in its current location, were the most common drivers of timely childhood vaccination. However, supply-side factors such as travel time to the nearest immunisation clinic, availability of cold-storage and staffing numbers in the nearest immunisation clinic were also significant determinants. Furthermore, the determinants varied across specific vaccines and the timing of doses. For example, delivery in a health facility (aOR = 1.58, 95 %CI: 1.02-2.53), living less than 30 min (aOR = 2.11, 95 %CI: 1.2-8.84) and living between 30 and 60 min (aOR = 3.68, 95 %CI: 1.1-14.99) from a fixed-immunisation clinic was associated with timely HepB0, a time-sensitive vaccine that must be administered within 24 h of birth. On the other hand, children who received Penta1 and Penta2 on time were three- to five-fold more likely to receive subsequent doses on time (Penta2 and Penta3, respectively). Finally, proximity to an immunisation facility with functional vaccine cold-storage was a significant supply-side determinant of timely MCV1 (aOR = 1.4, 95 %CI: 1.09-1.99). CONCLUSIONS: These findings provide valuable insights for programme managers and policymakers. By prioritising interventions and allocating scarce resources based on these identified determinants, they can maximize their impact and ensure children in The Gambia receive timely vaccinations throughout their first year of life, contributing to IA2030 goals.

Abstract: Despite substantial advances in global measles vaccination, measles disease burden remains high in many low- and middle-income countries. A key public health strategy for controlling measles in such high-burden settings is to conduct supplementary immunization activities (SIAs) in the form of mass vaccination campaigns, in addition to delivering scheduled vaccination through routine immunization (RI) programs. To achieve balanced implementations of RI and SIAs, robust measurement of subnational RI-specific coverage is crucial. In this paper we develop a space-time smoothing model for estimating RI-specific coverage of the first dose of measles-containing-vaccines (MCV1) at subnational level using complex survey data. The application that motivated this work is estimation of the RI-specific MCV1 coverage in Nigeria's 36 states and the Federal Capital Territory. Data come from four demographic and health surveys, three multiple indicator cluster surveys and two national nutrition and health surveys conducted in Nigeria between 2003 and 2018. Our method incorporates information from the SIA calendar published by the World Health Organization and accounts for the impact of SIAs on the overall MCV1 coverage, as measured by cross-sectional surveys. The model can be used to analyze data from multiple surveys with different data collection schemes and construct coverage estimates with uncertainty that reflects the various sampling designs. Implementation of our method can be done efficiently using integrated nested Laplace approximation (INLA).

Abstract: INTRODUCTION: Despite gains in global coverage of childhood vaccines, many children remain undervaccinated. Although mass vaccination campaigns are commonly conducted to reach these children their effectiveness is unclear. We evaluated the effectiveness of a mass vaccination campaign in reaching zero-dose children. METHODS: We conducted a prospective study in 10 health centre catchment areas in Southern province, Zambia in November 2020. About 2 months before a national mass measles and rubella vaccination campaign conducted by the Ministry of Health, we used aerial satellite maps to identify built structures. These structures were visited and diphtheria-tetanus-pertussis (DTP) and measles zero-dose children were identified (children who had not received any DTP or measles-containing vaccines, respectively). After the campaign, households where measles zero-dose children were previously identified were targeted for mop-up vaccination and to assess if these children were vaccinated during the campaign. A Bayesian geospatial model was used to identify factors associated with zero-dose status and measles zero-dose children being reached during the campaign. We also produced fine-scale zero-dose prevalence maps and identified optimal locations for additional vaccination sites. RESULTS: Before the vaccination campaign, 17.3% of children under 9 months were DTP zero-dose and 4.3% of children 9-60 months were measles zero-dose. Of the 461 measles zero-dose children identified before the vaccination campaign, 338 (73.3%) were vaccinated during the campaign and 118 (25.6%) were reached by a targeted mop-up activity. The presence of other children in the household, younger age, greater travel time to health facilities and living between health facility catchment areas were associated with zero-dose status. Mapping zero-dose prevalence revealed substantial heterogeneity within and between catchment areas. Several potential locations were identified for additional vaccination sites. CONCLUSION: Fine-scale variation in zero-dose prevalence and the impact of accessibility to healthcare facilities on vaccination coverage were identified. Geospatial modelling can aid targeted vaccination activities.

Abstract: BACKGROUND: There is always a risk of importing infectious diseases when travelling abroad. AIM: To estimate the effective risk of a Chilean of acquiring measles during a travel by countries where measles outbreaks have been reported, considering the present level of immunity in the country. MATERIAL AND METHODS: Previously established mathematical models using differential equations were applied to calculate the risk of acquiring measles of people traveling to endemic areas. RESULTS: The probability of acquiring measles of a voyager is 8.11 x 10-8. CONCLUSIONS: These estimations help decision making about preventive measures for travelers to endemic measles areas.

Abstract: OBJECTIVE: Using simulated outbreaks to choose the optimal model and its related parameters on measles so as to provide technical support for developing an Auto Warning System (AWS). METHODS: AEGIS-Cluster Creation Tool was applied to simulate a range of unique outbreak signals. Then these simulations were added to the actual daily counts of measles from the National Disease Surveillance System, between 2005 and 2007. Exponential weighted moving average (EWMA), C1-MILD (C1), C2-MEDIUM (C2), C3-ULTRA (C3) and space-time permutation scan statistic model were comprehensively applied to detect these simulations. Tools for evaluation as Youden' s index and detection time were calculated to optimize parameters before an optimal model was finally chosen. RESULTS: EWMA (lamda = 0.6, k = 1.0), CI (k = 0.1, H=3sigma), C2 (k = 0.1, H=3sigma), C3 (k = 1.0, H=4sigma) and space-time permutation scan statistic (maximum temporal cluster size=7 d, maximum spatial cluster size = 5 km) appeared to be the optimal parameters among these models. Youden's index of EWMA was 90.8% and detection time being 0.121 d. Youden's index of C1 was 88.7% and detection time being 0.142 d. Youden's index of C2 was 92.9% and detection time being 0.121 d. Youden's index of C3 was 87.9% and detection time being 0.058 d. Youden's index of space-time permutation scan statistic was 94.3% and detection time being 0.176 d. CONCLUSION: Among these five early warning detection models, space-time permutation scan statistic model had the highest efficacy.

Abstract: MMR (measles-mumps-rubella) immunization in Japan was suspended in 1993 due to the high incidence of mumps meningitis as a complication. As a result, immunization coverage for rubella still remains at the 50-60% level in Japan. One way to increase the coverage rate is to increase the frequency of immunization. We calculated the predicted positivity rate of the antibody and cost and the benefits is three models of double vaccination, i.e., vaccination twice. The first model consists of simply two identical vaccinations. The second model consists of two vaccinations with mass vaccination at school for the second immunization. The third model consists of two vaccinations with screening of the urinary antibody for rubella in the second immunization. To calculate the predicted values we used coefficients from Ibara City. The predicted positivity rates and cost increases ranged from 60% to 90% and from 7.3 billion to 12.8 billion yen from the first to third models, respectively. Screening for the urinary antibody should be much cheaper than the presumed price because more than a million subjects will be screened. Since it would cost less than half the price, the third model should be best for the positivity rate of the antibody and cost and benefits. Therefore, we think that third model is the best correction until MMR immunization can be reintroduced.

Abstract: Key epidemiological parameters (forces of infection, contact matrices, reproduction ratios) from the basic SEIR age structured model for childhood infectious diseases are estimated for all Italian regions from pre-vaccination case-notification data. Such parameters allow to summarise the pre-vaccination epidemiology of measles in the Italian regions, particularly the amount of effort needed for the eradication of the disease, consistently with the WHO targets. Despite the limited reliability paid to Italian case notifications data, the results show i) that the estimated eradication coverages are nor distant from the levels estimated from Northern-Europe; ii) that regions seemingly demanding the largest eradication effort seem also to be those characterised, up to now, by the lowest coverages; iii) the importance of achieving high coverages without delays in the age of administration of vaccination.

Abstract: OBJECTIVE: The study was aimed at comparing measles and rubella disease elimination levels in a homogeneous and heterogeneous population according to socioeconomic status with interactions amongst low- and high-income individuals and diversity in the average number of contacts amongst them. METHODS: Effective reproductive rate simulations were deduced from a susceptibleinfected- recovered (SIR) mathematical model according to different immunisation rates using measles (1980 and 2005) and rubella (1998 and 2005) incidence data from Latin-America and the Caribbean. Low- and high-income individuals' social interaction and their average number of contacts were analysed by bipartite random network analysis. MAPLE 12 (Maplesoft Inc, Ontario Canada) software was used for making the simulations. RESULTS: The progress made in eliminating both diseases between both periods of time was reproduced in the socially-homogeneous population. Measles (2005) would be eliminated in high- and low-income groups; however, it would only be achieved in rubella (2005) if there were a high immunity rate amongst the low-income group. If the average number of contacts were varied, then rubella would not be eliminated, even with a 95 % immunity rate. CONCLUSION: Monitoring the elimination level in diseases like measles and rubella requires that socio-economic status be considered as well as the population's interaction pattern. Special attention should be paid to communities having diversity in their average number of contacts occurring in confined spaces such as displaced communities, prisons, educational establishments, or hospitals.

Abstract: OBJECTIVE: To study the spatial-temporal dynamical features of measles in Zhejiang province. METHODS: Data was from the China Disease Surveillance Information System and China Immunization Program Information Management System. Power-law method on spatial-temporal-multicomponent model was used to analyze the epidemic characteristics of measles in the districts of Zhejiang province. RESULTS: The incidence of measles in Zhejiang province was 2.72/100 000 (1 494 cases) in 2013. Compared to the first order adjacent matrix, Power-law method showed a lower value of Akaike information criterion. The follow-up impact from the previous measles epidemic was strong to the Keqiao, Xiaoshan and Yuecheng districts with the autoregressive component as 1.39, 0.88 and 0.77, respectively. Local risk of measles seemed high in Keqiao, Qujiang and Xiaoshan districts with the endemic component as 4.06, 3.74 and 3.55, respectively. Impact of the epidemic to the nearby districts was large in Keqiao, Shangyu districts and Jiande city with epidemic components as 3.08, 2.54 and 2.21, respectively. CONCLUSION: The spatial-temporal feature of measles in several districts of Zhejiang province appeared heterogeneous, suggesting the specific strategies should be taken to control the epidemics of measles.