Abstract copyright UK Data Service and data collection copyright owner.To provide information on consumers' experience, knowledge and attitudes in relation to insurance policies of five different types. The types of policy covered are: 1. Life assurance policies 2. Endowment assurance policies 3. Home insurance (buildings) (Householders survey only) 4. Home insurance (contents) (Householders survey only) 5. Motor vehicle insurance Main Topics: Attitudinal/Behavioural Questions a) How people have taken out their present policy(ies) b) Who advised them and what advice they were given c) Policy holders' awareness of specific clauses in their policies d) Problems that have arisen when making a claim on their policy e) Other difficulties members of the public have had to face in connection with insurance f) Attitudes of non-policy holders to each type of policy and the way in which they would obtain such a policy. Survey consists of 4 separate questionnaires. Variables common to all include: whether informant holds a policy or whether he/she has ever held a policy - if applicable; reason for no longer having one is given. If respondent has never considered taking out a policy, reason' is stated and a record is made of how he/she would go about obtaining one should he/she so decide. Particulars of policy held: company with which policy is held; annual yearly premium on policy; method of payment (e.g. by Giro); whether paid by instalments; if so, frequency of payment is recorded; total sum assured on policy; date policy was taken out. Procedures followed when taking out policy: whether and by whom prompted to take out policy; objectives in taking out policy; decision stages involved in first taking out policy; from whom received advice (8 categories). There is a separate section on insurance brokers, where applicable. Knowledge of policy: particularly, cover of policy; facilities linked with policy (e.g. life insurance policy and mortgage facility); amount which would be received if respondent stopped paying premiums before policy maturation date. Also, familiarity with policy document is tested (e.g. the last time that holder read or even looked at the document, and where it is normally kept). Claims (except in life insurance questionnaire): number of claims made on policy; value of claim; whether handled by self; circumstances leading to claim; difficulties experienced; eventual outcome (e.g. claim met in full) and knowledge of the termaveraging' on claims. Attitudes: circumstances in which respondent might decide to increase the value of his/her policy; whether he/she has ever contacted the insurance company with a view to modifying policy (if yes, who contacted and number of times is recorded); whether respondent thinks that the company should contact policy holders from time to time or whether it should be left to the holder to contact the company. Satisfaction with people who may have been contacted at some stage in connection with policy is gauged on a 7-point scale (people listed are: insurance broker; insurance agent; bank manager; solicitor; other professional adviser). Background Variables Sex, age cohort, marital status, occupational details (including industry, job description and status, qualifications obtained), social grade, household status (i.e. sex, ages, number, occupational status, marital status, relationship to informant), home tenure and area of residence.

The McGRAW Life Insurance Data and Lead database provides access to 16 million individuals actively searching for life insurance. Our high-quality leads include 30, 60, 90-day aged data and up to 12 months of inquiries. Connect with high-intent consumers and boost your campaign effectiveness.

Business Problem: We all know that Health care is very important domain in the market. It is directly linked with the life of the individual; hence we have to be always be proactive in this particular domain. Money plays a major role in this domain, because sometime treatment becomes super costly and if any individual is not covered under the insurance then it will become a pretty tough financial situation for that individual. The companies in the medical insurance also want to reduce their risk by optimizing the insurance cost, because we all know a healthy body is in the hand of the individual only. If individual eat healthy and do proper exercise the chance of getting ill is drastically reduced. Goal & Objective: The objective of this exercise is to build a model, using data that provide the optimum insurance cost for an individual. You have to use the health and habit related parameters for the estimated cost of insurance

Review Parameters Review points 1) Introduction of the business problem a) Defining problem statement
b) Need of the study/project
c) Understanding business/social opportunity

2)Data Report
a) Understanding how data was collected in terms of time, frequency and methodology
b) Visual inspection of data (rows, columns, descriptive details)
c) Understanding of attributes (variable info, renaming if required)

3) Exploratory data analysis
a) Univariate analysis (distribution and spread for every continuous attribute, distribution of data in categories for categorical ones)
b) Bivariate analysis (relationship between different variables , correlations)
a) Removal of unwanted variables (if applicable)
b) Missing Value treatment (if applicable)
d) Outlier treatment (if required)
e) Variable transformation (if applicable)
f) Addition of new variables (if required)

4) Business insights from EDA a) Is the data unbalanced? If so, what can be done? Please explain in the context of the business
b) Any business insights using clustering (if applicable)
c) Any other business insights

This is a data set of individuals in Maryland, Michigan, New Mexico, Ohio, Puerto Rico, South Carolina, Texas, and Virginia who are looking for life insurance. The data can be segmented and ordered based on State, zip code, city, and age. The dates the data was collected were from 07/01/2022 - 10/04/2022. Please feel free to reach out if you have any questions about this data set.

The Iowa Insurance Division is responsible for issuing licenses or authority for many types of regulated individuals dealing with insurance related products. Individuals interested in becoming either a resident or non-resident insurance producer licensed in the state of Iowa need to apply through the National Insurance Producer Registry (NIPR) online system. Those wishing to become a resident insurance producer licensed in the state of Iowa must successfully pass the appropriate Iowa producer licensing exam for that specific line of authority.

To add additional lines of authority, a resident or non-resident insurance producer licensed in the state of Iowa need to apply through the NIPR online system. Resident insurance producers wishing to add a line of authority must successfully pass the appropriate Iowa producer licensing exam for that specific line of authority.

This dataset provides a listing of resident and non-resident insurance producers licensed to sell to Iowans.

Total-Liabilities Time Series for CNO Financial Group Inc. CNO Financial Group, Inc., through its subsidiaries, develops, markets, and administers health insurance, annuity, individual life insurance, insurance products, and financial services for middle-income pre-retiree and retired Americans in the United States. It offers Medicare supplement, supplemental health, and long-term care insurance policies; life insurance; and annuities, as well as Medicare advantage plans to individual consumers through phone, virtually, online, and face-to-face with agents. The company also focuses on sale of voluntary benefit life and health insurance products for businesses, associations, and other membership groups by interacting with customers at their place of employment. In addition, it provides fixed indexed annuities; fixed interest annuities, including fixed rate single and flexible premium deferred annuities; single premium immediate annuities; supplemental health products, such as specified disease, accident, and hospital indemnity products; and long-term care plans primarily to retirees, lesser degree, and older self-employed individuals in the middle-income market. Further, the company offers universal life and other interest-sensitive life products; and traditional life policies that include whole life, graded benefit life, term life, and single premium whole life products, as well as graded benefit life insurance products. It markets its products under the Bankers Life, Washington National, and Colonial Penn brand names. The company was founded in 1979 and is headquartered in Carmel, Indiana.

We have received explicit consent from our participants to publish the de-identified data in the public domain. We have de-identified the data by removing all individually identifying information (IP addresses, and for the six in-person participants in Study 2, their names) from data files before uploading them.

Fixed-Asset-Turnover Time Series for CNO Financial Group Inc. CNO Financial Group, Inc., through its subsidiaries, develops, markets, and administers health insurance, annuity, individual life insurance, insurance products, and financial services for middle-income pre-retiree and retired Americans in the United States. It offers Medicare supplement, supplemental health, and long-term care insurance policies; life insurance; and annuities, as well as Medicare advantage plans to individual consumers through phone, virtually, online, and face-to-face with agents. The company also focuses on sale of voluntary benefit life and health insurance products for businesses, associations, and other membership groups by interacting with customers at their place of employment. In addition, it provides fixed indexed annuities; fixed interest annuities, including fixed rate single and flexible premium deferred annuities; single premium immediate annuities; supplemental health products, such as specified disease, accident, and hospital indemnity products; and long-term care plans primarily to retirees, lesser degree, and older self-employed individuals in the middle-income market. Further, the company offers universal life and other interest-sensitive life products; and traditional life policies that include whole life, graded benefit life, term life, and single premium whole life products, as well as graded benefit life insurance products. It markets its products under the Bankers Life, Washington National, and Colonial Penn brand names. The company was founded in 1979 and is headquartered in Carmel, Indiana.

Context

MedCamp organizes health camps in several cities with low work-life balance. They reach out to working people and ask them to register for these health camps. For those who attend, MedCamp provides them the facility to undergo health checks or increase awareness by visiting various stalls (depending on the format of the camp).

MedCamp has conducted 65 such events over a period of 4 years and they see a high drop off between “Registration” and the Number of people taking tests at the Camps. In the last 4 years, they have stored data of ~110,000 registrations they have done.

One of the huge costs in arranging these camps is the amount of inventory you need to carry. If you carry more than the required inventory, you incur unnecessarily high costs. On the other hand, if you carry less than the required inventory for conducting these medical checks, people end up having bab experience.

The Process:

1. MedCamp employees/volunteers reach out to people and drive registrations.
2. During the camp, People who “ShowUp” either undergo the medical tests or visit stalls depending on the format of the health camp.

Other things to note:

• Since this is a completely voluntary activity for the working professionals, MedCamp usually has little profile information about these people.
• For a few camps, there was a hardware failure, so some information about the date and time of registration is lost.
• MedCamp runs 3 formats of these camps. The first and second format provides people with an instantaneous health score. The third format provides information about several health issues through various awareness stalls.

Favorable outcome:

• For the first 2 formats, a favorable outcome is defined as getting a health_score, while in the third format it is defined as visiting at least a stall.
• You need to predict the chances (probability) of having a favorable outcome.

Data Description

Train.zip contains the following 6 csv alongside the data dictionary that contains definitions for each variable

Health_Camp_Detail.csv – File containing Health_Camp_Id, Camp_Start_Date, Camp_End_Date and Category details of each camp.

Train.csv – File containing registration details for all the test camps. This includes Patient_ID, Health_Camp_ID, Registration_Date and a few anonymized variables as on registration date.

Patient_Profile.csv – This file contains Patient profile details like Patient_ID, Online_Follower, Social media details, Income, Education, Age, First_Interaction_Date, City_Type and Employer_Category

First_Health_Camp_Attended.csv – This file contains details about people who attended health camp of first format. This includes Donation (amount) & Health_Score of the person.

Second_Health_Camp_Attended.csv - This file contains details about people who attended health camp of second format. This includes Health_Score of the person.

Third_Health_Camp_Attended.csv - This file contains details about people who attended health camp of third format. This includes Number_of_stall_visited & Last_Stall_Visited_Number.

Test Set

Test.csv – File containing registration details for all the test camps. This includes Patient_ID, Health_Camp_ID, Registration_Date and a few anonymized variables as on registration date.

Train / Test split:

Camps started on or before 31st March 2006 are considered in Train Test data is for all camps conducted on or after 1st April 2006.

Sample Submission:

Patient_ID: Unique Identifier for each patient. This ID is not sequential in nature and can not be used in modeling

Health_Camp_ID: Unique Identifier for each camp. This ID is not sequential in nature and can not be used in modeling

Outcome: Predicted probability of a favorable outcome.

Evaluation Metric

The evaluation metric for this hackathon is ROC-AUC Score.

Total-Cashflows-From-Financing-Activities Time Series for The Baldwin Insurance Group, Inc.. The Baldwin Insurance Group, Inc. operates as an independent insurance distribution firm that delivers insurance and risk management solutions in the United States. It operates through three segments: Insurance Advisory Solutions; Underwriting, Capacity & Technology Solutions; and Mainstreet Insurance Solutions. The Insurance Advisory Solutions segment provides private risk management, commercial risk management, employee benefits, and Medicare insurance solutions for businesses and high-net-worth individuals, as well as their families. The Underwriting, Capacity & Technology Solutions segment offers MGA platform, that manufactures technology-enabled insurance products suite comprises personal, commercial, and professional lines. The Mainstreet Insurance Solutions segment provides personal insurance, commercial insurance, and life and health solutions to individuals and businesses in communities, as well as offers reinsurance brokerage; and consultation for government assistance programs and solutions, including traditional Medicare and Medicare advantage and affordable care act to seniors and eligible individuals through a network of primarily independent contractor agents. The company was formerly known as BRP Group, Inc. and changed its name to The Baldwin Insurance Group, Inc. in May 2024. The Baldwin Insurance Group, Inc. was founded in 2011 and is headquartered in Tampa, Florida.

Diluted-Average-Shares Time Series for CNO Financial Group Inc. CNO Financial Group, Inc., through its subsidiaries, develops, markets, and administers health insurance, annuity, individual life insurance, insurance products, and financial services for middle-income pre-retiree and retired Americans in the United States. It offers Medicare supplement, supplemental health, and long-term care insurance policies; life insurance; and annuities, as well as Medicare advantage plans to individual consumers through phone, virtually, online, and face-to-face with agents. The company also focuses on sale of voluntary benefit life and health insurance products for businesses, associations, and other membership groups by interacting with customers at their place of employment. In addition, it provides fixed indexed annuities; fixed interest annuities, including fixed rate single and flexible premium deferred annuities; single premium immediate annuities; supplemental health products, such as specified disease, accident, and hospital indemnity products; and long-term care plans primarily to retirees, lesser degree, and older self-employed individuals in the middle-income market. Further, the company offers universal life and other interest-sensitive life products; and traditional life policies that include whole life, graded benefit life, term life, and single premium whole life products, as well as graded benefit life insurance products. It markets its products under the Bankers Life, Washington National, and Colonial Penn brand names. The company was founded in 1979 and is headquartered in Carmel, Indiana.

Total-Liabilities Time Series for The Baldwin Insurance Group, Inc.. The Baldwin Insurance Group, Inc. operates as an independent insurance distribution firm that delivers insurance and risk management solutions in the United States. It operates through three segments: Insurance Advisory Solutions; Underwriting, Capacity & Technology Solutions; and Mainstreet Insurance Solutions. The Insurance Advisory Solutions segment provides private risk management, commercial risk management, employee benefits, and Medicare insurance solutions for businesses and high-net-worth individuals, as well as their families. The Underwriting, Capacity & Technology Solutions segment offers MGA platform, that manufactures technology-enabled insurance products suite comprises personal, commercial, and professional lines. The Mainstreet Insurance Solutions segment provides personal insurance, commercial insurance, and life and health solutions to individuals and businesses in communities, as well as offers reinsurance brokerage; and consultation for government assistance programs and solutions, including traditional Medicare and Medicare advantage and affordable care act to seniors and eligible individuals through a network of primarily independent contractor agents. The company was formerly known as BRP Group, Inc. and changed its name to The Baldwin Insurance Group, Inc. in May 2024. The Baldwin Insurance Group, Inc. was founded in 2011 and is headquartered in Tampa, Florida.

SUMMARY This table contains data about women, ages 15 to 50, pregnant people, infants, children, and youths, up to age 24. It contains information about a wide range of health topics, including medical conditions, nutrition, dehydration, oral health, mental health, safety, access to health care, and basic needs, like housing. Local, county-level prevalence rates, time trends, and health disparities about national public health priorities, including preterm birth, infant death, childhood obesity, adolescent depression and substance use, and high blood pressure, diabetes, and kidney disease in young adults. The population data is from the 2023-2024 San Francisco Maternal Child and Adolescent Health needs assessment and is published on the Open Data Portal to share with community partners, plan services, and promote health. For more information see: Maternal, Child, and Adolescent Health Homepage Maternal, Child, and Adolescent Health Reports HOW THE DATASET IS CREATED The Maternal, Child, and Adolescent Health (MCAH) Needs Assessment for San Francisco included review of a wide range of citywide population data covering a ten-year span, from 2014 to 2023. Data from over 83,000 birth records, 59,000 death records, 261,000 emergency room visits, 66,000 hospital admissions, and 90,000 newborn screening discharges were gathered, along with citywide data from child welfare records, health screenings in childcare and schools, DMV records of first-time drivers, school surveys, and a state-run mailed survey of recent births (California Department of Public Health MIHA survey). The datasets provided information about approximately 700 health conditions. Each health condition was described in terms of the number of people affected or cases, and the rate affected, stratified by age, sex, race-ethnicity, insurance status, zip code, and time period. Rates were calculated by dividing the number of people or events by the population group estimate (e.g., total births or census estimates), then multiplying by 100 or 1,000 depending on the measure. Each rate was presented with its 95% confidence interval to support users to compare any two rates, either between groups or over time. Two rates differ “significantly” if their 95% confidence intervals do not overlap. The present dataset summarizes the group-level results for any age-, sex-, race-, insurance-, zip code-, and/or period-specific group that included at least 20 people or cases. Causes of death, health conditions that affected over 1000 people in the time frame, problems that got worse over time, and health disparities by insurance, race-ethnicity and/or zip code were flagged for the MCAH Needs Assessment. UPDATE PROCESS The dataset will be updated manually, bi-annually, each December and June. HOW TO USE THIS DATASET Population data from the MCAH needs assessment are shared in several formats, including aggregated datasets on DataSF.gov, downloadable PDF summary reports by age group, interactive online visualizations, data tables, trend graphs, and maps. Information about each variable is available in a linked data dictionary. The definition of each numerator and denominator depends on data source, life stage, and time. Health conditions may not be directly comparable across life stage, if the numerator definition includes age- or pregnancy-specific diagnosis codes (e.g. diabetes hospitalization). For small groups or rare conditions, consider combining time periods and/or groups. Data are suppressed if fewer than 20 cases happened in the group and period. Group-specific rates are available if the matched group-specific census estimates (denominator) were available. Census estim

The Texas Department of Insurance (TDI) is responsible for licensing, registering, certifying, and regulating people who sell insurance or adjust property and casualty claims in Texas. This data set includes a row for each license held by a person. A person with more than one license will be listed in multiple rows. To view the list of agencies and business licensed by TDI, go to the Insurance agencies data set. To learn more about the type of licenses in this data set, go to TDI’s agent and adjuster licensing webpage.

The Pradhan Mantri Jeevan Jyoti Bima Yojana (PMJJBY) is a government-backed life insurance scheme launched by the Government of India. Designed to be affordable, it offers a life insurance cover of ₹2 lakh with an annual premium of just ₹330 for individuals aged between 18 to 50 years. The scheme is available to people with a savings bank account who give their consent to join and enable auto-debit. PMJJBY provides a death benefit to the nominee in case of the insured's demise, ensuring financial support to the family. The initiative aims to extend insurance coverage to a larger section of the Indian population, especially those who might not have access to or the means for conventional insurance products.

Consumer habits, shopping habits and the extent to which informed as consumer among older people with low income. Topics: financial support by family members; type and amount of planned expenditures; frequency of vacation trips; shopping habits; reasons for the selection of shopping place; price comparisons; expenditure planning at the start of the month; creating shopping lists; purchases in health food stores; perception of special offers; possible reasons to change regular store; purchases with mail-order firms; attitude to name-brand articles; participation and purchases at so-called ´sale demonstrations´ in restaurants; purchases from salesmen at the door; conduct with complaints; attitude to consumer education; information need with certain products; missing product offering; information and purchase behavior with larger acquisitions; utilization of consumer advice centers; comparison of personal consumer behavior earlier and today; critical consumer consciousness (scale); cost of living and savings habits; savings goals; attitude to installment purchases and purchases on credit; conduct with financial difficulties; economic restrictions at retirement of spouse from professional life or death of spouse; assessment of the economic situation of pensioners in the FRG; satisfaction with income; source of income and financial support by family members; leisure activities and media usage; paying attention to advertisments and advertising broadcasts; attitude to advertising (scale); purchase of unnecessary articles; judgement on personal condition of health; contact with doctors; confinement to bed; health insurance and life insurance; monthly expenditures for medication and medicinal preparations; Demography: age (classified); sex; age of spouse; marital status; school education; income; household income; sources of income; household composition; degree of urbanization; state; personal occupational situation and that of spouse; housing situation and size of residence; residence furnishings; rent costs; length of local residency; contacts with one´s children; attitude to age and non-involvement. Interviewer rating: shopping opportunities in vicinity of residence, willingness of respondent to cooperate, presence of other persons, length of interview; time of ending interview; city size; social class of respondent; number of contact attempts. Das Konsumverhalten, die Einkaufsgewohnheiten und die Informiertheit als Verbraucher bei älteren Menschen mit geringem Einkommen. Themen: Finanzielle Unterstützung durch Familienangehörige; Art und Höhe von geplanten Ausgaben; Häufigkeit von Urlaubsreisen; Einkaufsgewohnheiten; Gründe für die Wahl der Einkaufsstätte; Preisvergleiche; Ausgabenplanung am Monatsanfang; Erstellen von Einkaufslisten; Käufe in Reformhäusern; Wahrnehmung von Sonderangeboten; mögliche Gründe für den Wechsel des Stammgeschäfts; Käufe bei Versandhäusern; Einstellung zu Markenartikeln; Teilnahme und Käufe bei sogenannten "Verkaufsvorführungen" in Gaststätten; Käufe bei Vertretern an der Tür; Verhalten bei Reklamationen; Einstellung zur Verbraucheraufklärung; Informationsbedürfnis bei bestimmten Produkten; vermißtes Warenangebot; Informations- und Kaufverhalten bei größeren Anschaffungen; Inanspruchnahme von Verbraucherberatungsstellen; Vergleich des eigenen Verbraucherverhaltens früher und heute; kritisches Verbraucherbewußtsein (Skala); Lebenshaltungskosten und Sparverhalten; Sparziele; Einstellung zu Ratenkäufen und Käufen auf Kredit; Verhalten bei finanziellen Schwierigkeiten; wirtschaftliche Einschränkungen beim Ausscheiden des Ehepartners aus dem Berufsleben bzw. Tod des Ehepartners; Einschätzung der wirtschaftlichen Situation der Rentner in der BRD; Einkommenszufriedenheit; Einkommensquelle und finanzielle Unterstützung durch Familienangehörige; Freizeitaktivitäten und Mediennutzung; Beachtung von Werbeanzeigen und Werbesendungen; Einstellung zur Werbung (Skala); Kauf von unnötigen Artikeln; Beurteilung des eigenen Gesundheitszustandes; Arztkontakte; Bettlägerigkeit; Krankenversicherung und Lebensversicherung; monatliche Ausgaben für Medikamente und Heilpräparate. Demographie: Alter (klassiert); Geschlecht; Alter des Ehepartners; Familienstand; Schulbildung; Einkommen; Haushaltseinkommen; Einkommensquellen; Haushaltszusammensetzung; Urbanisierungsgrad; Bundesland; eigene berufliche Situation und die des Ehepartners; Wohnsituation und Wohnungsgröße; Wohnungsausstattung; Mietkosten; Dauer der Ortsansässigkeit; Kontakte zu den Kindern; Einstellung zum Alter und Disengagement. Interviewerrating: Einkaufsmöglichkeiten in der Nähe der Wohnung, Kooperationsbereitschaft des Befragten, Anwesenheit anderer Personen, Interviewdauer; Zeitpunkt des Interviewabbruchs; Ortsgröße; Schichtzugehörigkeit des Befragten; Anzahl der Kontaktversuche.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

What is term life insurance?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

NOTE: As of 12/17/2024, this dataset is no longer updated. Please use ASPR Treatments Locator. This dataset displays pharmacies, clinics, and other locations with safe and effective COVID-19 medications. These medications require a prescription from a healthcare provider. Some locations, known as Test to Treat sites, give you the option to get tested, get assessed by a healthcare provider, and receive treatment – all in one visit. COVID-19 medications may be available at additional locations that are not shown in this dataset. The locations displayed have either self-attested they have inventory of Paxlovid (nirmatrelvir packaged with ritonavir), Lagevrio (molnupiravir), or Veklury (Remdesivir) within at least the last two months and/or reported participation in the Paxlovid Patient Assistance Program. Sites that have not reported in the last two weeks display a notification, "Inventory has not been reported in the last 2 weeks. Please contact the provider to make sure the product is available." Outpatient COVID-19 medications may be available at additional locations not listed on this website. All therapeutics identified in the locator not approved by the FDA must be used in alignment with the terms of the respective product’s Emergency Use Authorization. Visit COVID-19 Treatments and Therapeutics for more information on all treatment options. This website identifies sites that have commercially purchased inventory of COVID-19 treatments and, in some cases, may identify sites that have remaining, no-cost U.S. government distributed supply. Some sites may charge for services not covered by insurance. Some sites may offer telehealth services. This website is intended for informational purposes only and does not serve as an endorsement or recommendation for use of any of the locations listed on the sites. Clarification for DoD Facilities: Those individuals eligible for care in an MTF include Active Duty Service Members (ADSMs), covered beneficiaries enrolled in TRICARE Prime or Select, including TRICARE Reserve Select (TRS), TRICARE Retired Reserve (TRR) and TRICARE Young Adult (TYA) participants, TRICARE for Life beneficiaries, and individuals otherwise entitled by law to MTF care (e.g., regular retired members and their dependents who are not enrolled in TRICARE but who are otherwise eligible for MTF space-available care, certain foreign military members and their families registered in DEERS, and others).

Non-Life Insurance Claims Cash Flows for a Synthetic Portfolio

Data generated with the Simulation Machine (Version 1) by Andrea Gabrielli and Mario V. Wüthrich, implemented by Kevin Kuo at Kasa.ai (Sept 2019): https://github.com/kasaai/simulationmachine (used) and https://people.math.ethz.ch/~wueth/simulation.html

Parameter Overview:

simulation_machine(num_claims = 100000, lob_distribution = c(0.25, 0.25, 0.30, 0.20), inflation = c(0.01, 0.01, 0.01, 0.01), sd_claim = 0.85, sd_recovery = 0.75)

Documentation:

Kasa AI Blog "simulationmachine: Synthesizing Individual Claims Data", Sept. 26, 2019: https://blog.kasa.ai/posts/simulation-machine/ and "An Individual Claims History Simulation Machine" by Andrea Gabrielli and Mario V. Wüthrich, winners of the "risks 2019 best paper award", https://www.mdpi.com/2227-9091/6/2/29

First, a caveat: the NFIP data does NOT provide information specific to individual homes or parcels. This information is protected under federal law. All personal identifying information about policy holders has been redacted, and data has been anonymized to census tract, reported ZIP code, and one decimal point digit of latitute and longitude. If mapped, flood insurance policies and claims may appear to be clustered at a particular location due to this anonymization. What all that means: you cannot search for an address to see whether it has flooded. However, among many things, this data shows flooding trends in Norfolk over the last 40+ years. It shows the census tracts that flood most frequently. And it shows where the largest number and highest value of claims occur.

FEMA believes this historic release of NFIP data promotes transparency, reduces complexity related to public data requests, and improves how stakeholders interact with and understand the program. This is the largest, most comprehensive release of NFIP data coordinated by FEMA to date. This dataset allows for customizable searches to create reports, analyze and visualize present and historical NFIP data faster and easier than before. This data will help FEMA build a national culture of preparedness by providing claims and policy information people need to make better choices about their flood risk and the insurance they need to protect the life they've built. Norfolk's Open Data team extracted city-specific information from the FEMA dataset. The dataset included here represents almost 6,000 claims on record from 1977 through 2019, totaling 67 million dollars in damage in the City of Norfolk.

To view the most updated version of the dataset, please click here: https://data.norfolk.gov/Government/FEMA-NFIP-Claims/suf7-r643/about_data