Information about school immunization requirements and data

Mass Vaccination Clinic Software Market Outlook

According to our latest research, the global Mass Vaccination Clinic Software market size reached USD 1.29 billion in 2024, reflecting robust adoption across healthcare systems worldwide. The market is poised to expand at a CAGR of 13.8% from 2025 to 2033, driven by digital transformation in public health and the rising need for efficient immunization management. By 2033, the market is forecasted to reach USD 4.11 billion. The key growth factor fueling this expansion is the increasing demand for streamlined, scalable, and secure solutions to manage large-scale vaccination drives, particularly in response to global health emergencies and routine immunization programs.

A primary driver of the Mass Vaccination Clinic Software market is the growing emphasis on public health preparedness and rapid response to pandemics. The COVID-19 pandemic underscored the necessity for robust digital infrastructure capable of managing unprecedented vaccination volumes, scheduling, supply chain logistics, and real-time reporting. Governments and healthcare organizations are investing heavily in advanced software platforms that can automate registration, consent, inventory tracking, and follow-up reminders. This shift towards digitization ensures not only operational efficiency but also enhances patient safety and data integrity. Additionally, the integration of analytics and artificial intelligence with mass vaccination clinic software enables authorities to monitor vaccine coverage, identify gaps, and optimize resource allocation, further driving market growth.

Another significant growth factor is the expanding scope of immunization programs beyond COVID-19. With increasing incidences of seasonal influenza, measles outbreaks, and the introduction of new vaccines for diseases such as HPV and meningitis, healthcare providers are seeking comprehensive solutions that can support multiple vaccination campaigns simultaneously. The ability of mass vaccination clinic software to handle diverse immunization schedules, manage multi-dose regimens, and ensure compliance with regulatory standards makes it indispensable for large-scale public health initiatives. Furthermore, collaborations between public and private sectors, coupled with funding from international health organizations, are accelerating software adoption in both developed and emerging markets.

The proliferation of cloud-based deployment models is also propelling the Mass Vaccination Clinic Software market. Cloud solutions offer scalability, remote access, and seamless integration with existing health information systems, making them ideal for geographically dispersed vaccination sites and mobile clinics. These platforms facilitate real-time data sharing and interoperability, enabling coordinated efforts across multiple stakeholders including hospitals, pharmacies, community health centers, and government agencies. As cybersecurity and data privacy regulations become more stringent, vendors are investing in advanced security features and compliance certifications, further boosting market confidence and adoption rates.

In the realm of digital solutions for healthcare, Travel Vaccine Management Software is emerging as a pivotal tool. This software is designed to streamline the process of administering vaccines to travelers, ensuring that they receive the necessary immunizations before visiting specific regions. With the increasing globalization and the rise in international travel, there is a growing need for efficient systems that can manage vaccine schedules, track patient histories, and provide reminders for upcoming doses. Travel Vaccine Management Software not only aids healthcare providers in maintaining comprehensive records but also enhances patient engagement by offering easy access to vaccination information. As more people venture abroad, the demand for such specialized software is expected to rise, contributing to the broader landscape of vaccination management solutions.

Regionally, North America continues to lead the global market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. This dominance is attributed to the presence of advanced healthcare infrastructure, high digital literacy, and proactive government initiatives for mass immunization. However, Asia Pacific is anticipated to witness the highest CAGR over the forecast period, dri

Poisson model of individual- and zip code-level factors associated with receipt of primary COVID-19 vaccination series and booster.

Mass Vaccination Clinic Software Market Outlook

According to our latest research, the global mass vaccination clinic software market size reached USD 1.42 billion in 2024, driven by the ongoing demand for efficient immunization management and digital transformation in healthcare. The market is poised for robust expansion, projected to grow at a CAGR of 13.8% from 2025 to 2033. By 2033, the market is forecasted to attain a value of USD 4.18 billion, reflecting the increasing adoption of digital solutions for mass immunization campaigns, particularly in response to public health emergencies and the growing focus on preventive healthcare. This growth is underpinned by the rising need for streamlined vaccine administration, data management, and reporting capabilities among healthcare providers worldwide.

A key driver propelling the mass vaccination clinic software market is the global emphasis on public health preparedness and rapid response to infectious disease outbreaks. The COVID-19 pandemic has highlighted the necessity for robust digital platforms that can manage large-scale vaccination campaigns efficiently. Governments and health organizations have accelerated their investments in technology to support vaccination logistics, appointment scheduling, inventory management, and real-time data tracking. This has resulted in an unprecedented surge in demand for mass vaccination clinic software, as these platforms facilitate seamless coordination among healthcare professionals, reduce administrative burdens, and ensure accurate record-keeping. The integration of analytics and reporting tools further enhances the ability of authorities to monitor vaccination progress and coverage, ultimately improving public health outcomes.

Another significant growth factor is the increasing adoption of cloud-based solutions in the healthcare sector. Cloud-based mass vaccination clinic software offers scalability, accessibility, and cost-effectiveness, making it an attractive option for organizations of all sizes. These platforms enable healthcare providers to access real-time data from multiple locations, streamline patient registration and consent processes, and ensure compliance with regulatory requirements. The flexibility of cloud deployment supports rapid implementation and integration with other healthcare systems, such as electronic health records (EHRs) and national immunization registries. As healthcare organizations prioritize digital transformation, the shift towards cloud-based solutions is expected to accelerate, further driving the expansion of the mass vaccination clinic software market.

The market is also experiencing growth due to the increasing awareness of the importance of routine immunization and preventive healthcare. Beyond pandemic response, governments and health agencies are investing in digital solutions to manage routine vaccination programs for diseases such as influenza, measles, and polio. The ability of mass vaccination clinic software to automate appointment scheduling, track vaccine inventory, and generate comprehensive reports is critical for improving vaccine coverage rates and ensuring timely administration. Additionally, the rise of community health initiatives and the involvement of non-traditional providers, such as pharmacies and community health centers, are broadening the end-user base for these solutions. This diversification of application areas is expected to sustain long-term growth in the market.

From a regional perspective, North America currently holds the largest share of the mass vaccination clinic software market, supported by advanced healthcare infrastructure, high digital literacy, and proactive government initiatives. Europe follows closely, with significant investments in public health digitization and cross-border immunization efforts. The Asia Pacific region is anticipated to witness the fastest growth during the forecast period, driven by increasing healthcare expenditure, expanding immunization programs, and the adoption of digital health solutions in emerging economies. Latin America and the Middle East & Africa are also showing promising growth trajectories, albeit from a smaller base, as governments in these regions prioritize public health modernization and disease prevention.

Deployment Mode Analysis

The deployment mode segment of the mass vaccination clinic software market is bifurcated into cloud-based and on-premises solutions, each addressing distinct organizational nee

Characteristics of individuals receiving a booster vaccination.

Note: As of November 10, 2023, this dataset has been archived. For the current version of this data, please visit: https://health.data.ny.gov/d/gikn-znjh

This dataset reports daily on the number of people vaccinated by New York providers with at least one dose and with a complete COVID-19 vaccination series overall since December 14, 2020. New York providers include hospitals, mass vaccination sites operated by the State or local governments, pharmacies, and other providers registered with the State to serve as points of distribution.

This dataset is created by the New York State Department of Health from data reported to the New York State Immunization Information System (NYSIIS) and the New York City Citywide Immunization Registry (NYC CIR). County-level vaccination data is based on data reported to NYSIIS and NYC CIR by the providers administering vaccines. Residency is self-reported by the individual being vaccinated. This data does not include vaccine administered through Federal entities or performed outside of New York State to New York residents. NYSIIS and CIR data is used for county-level statistics. New York State Department of Health requires all New York State vaccination providers to report all COVID-19 vaccination administration data to NYSIIS and NYC CIR within 24 hours of administration.

Note: As of 1/22/25, this dataset is no longer updated. This dataset reports the number of people vaccinated by New York providers with at least one dose and with a complete COVID-19 vaccination series overall since December 14, 2020. Currently, three COVID-19 vaccines have been authorized for emergency use by the FDA and approved by New York State's independent Clinical Advisory Task Force: one that was developed by Pfizer and BioNTech, a second that was developed by Moderna and a third that was developed by Janssen/Johnson & Johnson. New York providers include hospitals, mass vaccination sites operated by the State or local governments, pharmacies, and other providers registered with the state to serve as points of distribution. This dataset is created by the New York State Department of Health from data reported to the New York State Immunization Information System (NYSIIS) and the New York City Citywide Immunization Registry (NYC CIR). NYSIIS and CIR are confidential, secure, web-based systems that collect and maintain immunization information in one consolidated record for persons of all ages in NYS governed by Public Health Law 2168. Health care providers are required, by law, to enter all vaccines administered to children up to age 19. Immunizations administered to adults 19 and older may be reported with consent. New York State Department of Health requires all New York State vaccination providers to report all COVID-19 vaccination administration data to NYSIIS and NYC CIR within 24 hours of administration. Vaccination data by age is based on address data reported to NYSIIS and NYC CIR by the providers administering vaccines. Age is calculated by subtracting the dob from the date of vaccination. Note that COVID-19 vaccine availability greatly expanded for the different age groups over the period of time this dataset covers. This data does not include vaccine administered through Federal entities or performed outside of New York State to New York residents. NYSIIS and CIR data is used for age group statistics. This dataset is updated weekly.

Characteristics of individuals completing the primary series.

Abstract

To understand the socio-economic impact of COVID-19 and associated government measures over the long term, the fourth round of the COVID-19 National Panel Phone Survey 2020 was collected by the National Institute of Statistics of Djibouti (INSD) between March 11 and April 25, 2021. Various channels of impact are explored such as job loss, availability and price changes of basic food items, ability to access healthcare and education, food insecurity. The survey also includes a section on gender issues, including time-use and decision making, as well as a section on attitudes towards COVID-19 Vaccine. Within households, a respondent was chosen at random between the household heads and spouses, allowing comparison between female and male respondents in the sample. Further, the education questions are asked for a randomly chosen boy or girl within the households that have children.

Geographic coverage

Urban areas only. The survey is representative of the bottom 80 percent of the consumption distribution of the national households (thus the top 20 percent are excluded). It is representative by poverty status and by three domains of Balbala, rest of Djibouti city and urban areas outside Djibouti city.

Analysis unit

• Household
• Individual

Universe

The survey covers national households that reported telephone numbers, are included in the social registry data collected by the Ministry of Social Affairs and Solidarity (MASS) and have been interviewed after 2017.

Kind of data

Sample survey data [ssd]

Sampling procedure

As a recently conducted representative household survey with telephone numbers was not available, data from the national social registry collected by the Ministry of Social Affairs (MASS) was used as the sampling frame of the national sample. The social registry is an official database of households in Djibouti that may benefit from public transfers and be particular targets of poverty alleviation efforts. The sample consists of households drawn randomly from the social registry data restricted to urban households having at least one phone number and interviewed after July 1, 2017. The sample design is a one-stage probability sample selected from the sampling frame and stratified along two dimensions: the survey domain (three categories) and the poverty status (binary). This yields six independent strata. Within each stratum, households are selected with the same ex-ante probability but this differs across strata. The fourth wave sample consists of 1,561 respondents, 1,122 of which are panel households interviewed in wave 3, and 439 replacement households. The response rate of the whole sample stands at 71.8 percent. Unlike the third wave, in the fourth wave, households who were not reachable in wave 3 but were part of the first two waves, were considered as part of the sampling frame

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire of the fourth round is adapted from the questionnaire of the third round and in accordance with the template questionnaire prepared by the Poverty and Equity GP to measure the impact of COVID-19 on household welfare. It was designed in French and dispensed in local languages (Afar, Arabic, Somali, French or other). The questionnaire includes the following sections: - Household Roster - Employment - Household's Income Sources - Access to Basic Goods - Access to Healthcare and Education - Food Insecurity - Vaccine Attitudes - Gender

Cleaning operations

The CsPro CATI data entry application helped to enforce skip and range patterns during data collection. Standard consistency checks (like age differences between parents and children and unicity of household heads) were carried out at the time of the data collection. Because the entry application was strictly system-controlled, complete cases including missing items were avoided. The various checks resulted in a limited need for secondary data editing, which eventually entailed two main steps from the WB team. First, duplicated names of household members, who were otherwise distinct, were corrected by adding a suffix “bis” to the names. Second, after analysis of text responses mentioned in the residual “other” categories, a few items codes were adjusted (not exceeding 10 in any category).

Response rate

The response rate of the whole sample stands at 71.8 percent, with variations across location. In Balbala region, the rate was 75.1 percent, in the rest of Djibouti City, 71.6 percent, in other urban areas, it was 68.8 percent.

BackgroundSporadic outbreaks of COVID-19 remain a threat to public healthcare, especially if vaccination levels do not improve. As Malaysia begins its transition into the endemic phase, it is essential to identify the key determinants of COVID-19 vaccination intention amongst the pockets of the population who are still hesitant. Therefore, focusing on a sample of individuals who did not register for the COVID-19 vaccination, the current study integrated two widely used frameworks in the public health domain—the health belief model (HBM) and the theory of reasoned action (TRA)—to examine the inter-relationships of the predictors of vaccination intention amongst these individuals.MethodologyPrimary data from 117 respondents who did not register for the COVID-19 vaccination were collected using self-administered questionnaires to capture predictors of vaccination intention amongst individuals in a Malaysian context. The partial least squares structural equation modeling (PLS-SEM) technique was used to analyze the data.ResultsSubjective norms and attitude play key mediating roles between the HBM factors and vaccination intention amongst the unregistered respondents. In particular, subjective norms mediate the relationship between cues to action and vaccination intention, highlighting the significance of important others to influence unregistered individuals who are already exposed to information from mass media and interpersonal discussions regarding vaccines. Trust, perceived susceptibility, and perceived benefits indirectly influence vaccination intention through attitude, indicating that one’s attitude is vital in promoting behavioral change.ConclusionThis study showed that the behavioral factors could help understand the reasons for vaccine refusal or acceptance, and shape and improve health interventions, particularly among the vaccine-hesitant group in a developing country. Therefore, policymakers and key stakeholders can develop effective strategies or interventions to encourage vaccination amongst the unvaccinated for future health pandemics by targeting subjective norms and attitude.

Russia was to export 250 million doses of the coronavirus (COVID-19) vaccine Sputnik V to India, which was among the major planned producers of the vaccine. In total, Indian companies planned to produce at least 1,152 million doses of Sputnik V per year. Furthermore, Mexico ordered a total of 24 million doses of the vaccine. Sputnik V was authorized in more than 70 countries worldwide as of January 2022. Russia applied for the vaccine approval in the European Union in January 2021, while several EU countries approved its use earlier, such as Hungary or Slovakia.

Russia's first vaccine against COVID-19 In August 2020, Russia registered Sputnik V, the world’s first approved vaccine against COVID-19, which was developed at Gamaleya Research Institute in Moscow. After the third phase of clinical trials, the vaccine's effectiveness was measured at 91.6 percent. The mass vaccination in Russia started in January 2021. In December 2020, 30 percent of Russians would get vaccinated against COVID-19 with Sputnik V.
Is Sputnik V available abroad? In total, over 50 countries worldwide placed orders for Sputnik V from Russia. As of January 2021, The vaccine was used for vaccination in Russia, Belarus, Serbia. In October 2020, Russia applied for prequalification of Sputnik V at the WHO to speed up its availability worldwide. Other countries that would produce the vaccine, such as Brazil, China, India, or the Republic of Korea, would also sell it abroad. Among several countries surveyed in November 2020, the highest level of awareness about Sputnik V was recorded in Mexico.

Approximately 3.8 million Russians received both doses of Sputnik V, a vaccine against the coronavirus (COVID-19) developed by the Gamaleya Research Center as of March 31, 2021. The voluntary vaccination began in early December 2020, and the mass national campaign started on January 18, 2021 for adults from every age group. The first COVID-19 vaccine registered in Russia and worldwide, Sputnik V was also exported to other countries.

BackgroundSporadic outbreaks of COVID-19 remain a threat to public healthcare, especially if vaccination levels do not improve. As Malaysia begins its transition into the endemic phase, it is essential to identify the key determinants of COVID-19 vaccination intention amongst the pockets of the population who are still hesitant. Therefore, focusing on a sample of individuals who did not register for the COVID-19 vaccination, the current study integrated two widely used frameworks in the public health domain—the health belief model (HBM) and the theory of reasoned action (TRA)—to examine the inter-relationships of the predictors of vaccination intention amongst these individuals.MethodologyPrimary data from 117 respondents who did not register for the COVID-19 vaccination were collected using self-administered questionnaires to capture predictors of vaccination intention amongst individuals in a Malaysian context. The partial least squares structural equation modeling (PLS-SEM) technique was used to analyze the data.ResultsSubjective norms and attitude play key mediating roles between the HBM factors and vaccination intention amongst the unregistered respondents. In particular, subjective norms mediate the relationship between cues to action and vaccination intention, highlighting the significance of important others to influence unregistered individuals who are already exposed to information from mass media and interpersonal discussions regarding vaccines. Trust, perceived susceptibility, and perceived benefits indirectly influence vaccination intention through attitude, indicating that one’s attitude is vital in promoting behavioral change.ConclusionThis study showed that the behavioral factors could help understand the reasons for vaccine refusal or acceptance, and shape and improve health interventions, particularly among the vaccine-hesitant group in a developing country. Therefore, policymakers and key stakeholders can develop effective strategies or interventions to encourage vaccination amongst the unvaccinated for future health pandemics by targeting subjective norms and attitude.

Context

Most of the datasets about covid19 vaccination data on Kaggle are not available citywise. So here I am to your rescue!! This version is just a starter with data for 9 cities. I plan to upload data for almost every city in India in the upcoming versions.

Vaccination planning has been a challenge in India. Earlier in the year, individual Indian citizens had to register on the Cowin or Aarogya Setu portal in order to receive a COVID-19 vaccination. The limited number of vaccination slots resulted in fewer administrations during the initial 5 months of the vaccination programme (phase 1–4). The Government of India has now amended the vaccination policy by waiving the preregistration requirement and offering free vaccinations to accelerate the programme. However, mass gatherings in healthcare settings might lead to a further surge in daily cases. Door-to-door vaccination might be a feasible and safe solution to avoid such assemblies.

Attribute Information

|: Date column. Contains date from 26 April,2020 to 31st Oct, 2021. || : Contains info about two variants of COVID: delta and delta7(delta7 is delta+ actually) ||_confirmed: Cases confirmed ||_deceased: Number of deaths reported ||_recovered: Cases recovered ||_tested: Number of people tested ||_vaccinated1: 1st dose of vaccine administered ||_vaccinated2: 2nd dose of vaccine administered |_total_confirmed: this column does not carry any information(did not remove it to maintain the originality of data) |_total_deceased: this column does not carry any information(did not remove it to maintain the originality of data) |_total_recovered: this column does not carry any information(did not remove it to maintain the originality of data)

More about the data

There are many NaN values in the data. They are not there because there is some error in the data. Vaccination dri drive started in India from Jan, 2021. So data for vaccination will be available from Jan,2021.

I am planning to upload the data for more cities in upcoming versions. If you want data of some specific city in India, ask for it in the discussion.

Abstract

To understand the socio-economic impact of COVID-19 and associated government measures over the long term, the third round of the COVID-19 National Panel Phone Survey 2020 was collected by the National Institute of Statistics of Djibouti (INSD) between December 20, 2020 and February 2, 2021. In addition to the national panel sample, a sample of refugee and asylum-seeker households present in Djibouti was included in the data collection in order to capture the impact of COVID-19 on this precarious population. Various channels of impact are explored such as job loss, availability and price changes of basic food items, ability to access healthcare, and food insecurity. Compared to the second round of data collection, this survey includes questions on risk coping strategies as well as attitudes towards a potential vaccine against COVID-19.

Geographic coverage

Regarding the refugee sample, the survey is representative of the population of refugees and asylum-seekers present in Djibouti in three refugee villages (or refugee settlements) of Ali Addeh, Holl Holl and Markazi, as well as in the capital city Djibouti-city. Therefore, the survey covers both urban refugees (from Djibouti-city) and village-based refugees (from the refugee villages).

Analysis unit

• Household
• Individual

Universe

For the refugee sample, the survey covers households from the sample of the Refugee Survey collected in 2019 by INSD jointly with MASS, World Food Program (WFP) and United Nations High Commissioner for Refugees (UNHCR) through face-to-face interviews. The resultant refugee sample of the COVID-19 Phone Survey only includes households who had a phone number, and have responded to the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

Refugee Sample: The sample covers households from the sample of the Refugee Survey collected in 2019 by INSD jointly with MASS, World Food Program (WFP) and United Nations High Commissioner for Refugees (UNHCR) through face-to-face interviews. The original sample of the Refugee Survey in 2019 was drawn from the refugee registration data. The non-response rate stands at 39.5 percent for the refugee households. Among the Refugees Survey Sample, the refugee sample of the COVID-19 survey has not been drawn randomly but by selecting the households that have a phone number. The refugee sample of the third wave of the COVID-19 survey consisted of 564 interviewed households with complete information.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire of the third round is adapted from the questionnaire of the second round and in accordance with the template questionnaire prepared by the Poverty and Equity Global Practice to measure the impact of COVID-19 on household welfare. It was designed in French and dispensed in local language (Afar, Arabic, Somali, French or other). The questionnaire is the same for both national and refugee samples. The questionnaire includes the following sections: - Household Roster - Employment - Household's Income - Needs - Access - Safety Nets - Food Insecurity - Shock Coping Strategies - Opinion on Vaccine

Cleaning operations

The CsPro CATI data entry application helped to enforce skip and range patterns during data collection. Standard consistency checks (like age differences between parents and children and unicity of household heads) were carried out at the time of the data collection. Because the entry application was strictly system-controlled, complete cases including missing items were avoided. The various checks resulted in a limited need for secondary data editing, which eventually entailed two main steps from the WB team. First, duplicated names of household members, who were otherwise distinct, were corrected by adding a suffix “bis” to the names. Second, after analysis of text responses mentioned in the residual “other” categories, a few items codes were adjusted (not exceeding 10 in any category).

The data has been anonymized to ensure protection of the privacy of respondents in this dataset. All direct identifiers and string variables have been removed, recoding and topcoding of age, household size, and relation to household head were implemented. Local suppression of data to certain employment characteristics was also applied to achieve the required level of k-anonymity for a public use data file.

Response rate

Among the refugee sample, the response rate stood at 60.5% with 564 interviewed households.

The global emergence of coronavirus disease 2019 (COVID-19) posed unprecedented challenges, jeopardizing decades of progress in healthcare systems, education, and poverty eradication. While proven interventions such as handwashing and mass vaccination offer effective means of curbing COVID-19 spread, their uptake remains low, potentially undermining future pandemic control efforts. This systematic review synthesized available evidence of the factors influencing vaccine uptake and handwashing practices in Kenya, Uganda, and Tanzania in the context of COVID-19 prevention and control. We conducted an extensive literature search across PubMed, Science Direct, and Google Scholar databases following Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Out of 391 reviewed articles, 18 were eligible for inclusion. Some of the common barriers to handwashing in Kenya, Uganda, and Tanzania included lack of trust in the government’s recommendations or messaging on the benefits of hand hygiene and lack of access to water, while some of the barriers to vaccine uptake included vaccine safety and efficacy concerns and inadequate awareness of vaccination sites and vaccine types. Enablers of handwashing practices encompassed hand hygiene programs and access to soap and water while those of COVID-19 vaccine uptake included improved access to vaccine knowledge and, socio-economic factors like a higher level of education. This review underscores the pivotal role of addressing these barriers while capitalizing on enablers to promote vaccination and handwashing practices. Stakeholders should employ awareness campaigns and community engagement, ensure vaccine and hygiene resources’ accessibility, and leverage socio-economic incentives for effective COVID-19 prevention and control.Clinical trial registration: [https://clinicaltrials.gov/], identifier [CRD42023396303].