{"id":4870,"date":"2020-04-09T11:28:28","date_gmt":"2020-04-09T15:28:28","guid":{"rendered":"https:\/\/shvs.org\/?p=4870"},"modified":"2024-03-09T08:48:44","modified_gmt":"2024-03-09T13:48:44","slug":"state-covid-19-data-dashboards","status":"publish","type":"post","link":"https:\/\/shvs.org\/state-covid-19-data-dashboards\/","title":{"rendered":"State COVID-19 Data Dashboards"},"content":{"rendered":"<p><em>Emily Zylla and Lacey Hartman, SHADAC<\/em><\/p>\n<p>Accurate, timely data is a key tool in states\u2019 efforts to understand and respond to the impact of the coronavirus (COVID-19) outbreak at the local level. There have also been increasing calls to further break down COVID-10 data into subcategories (such as by gender, age, and race and ethnicity) in order to track the impact of the disease on specific populations. As of April 6, all 50 states and DC are publicly reporting some type of data related to COVID-19, such as the number of positive tests and\/or the number of deaths. Furthermore, some states have recently begun to utilize innovative dashboards in order visualize and track reported cases of coronavirus disease as well as monitor additional related key indicators. These dashboards are designed to organize complex data in an easy-to-digest visual format, allowing the audience to easily interpret key trends and patterns at a glance (e.g., see SHADAC\u2019s COVID-19 dashboard template, which is currently under development using mock data).<\/p>\n<p>This expert perspective reviews the key indicators currently being tracked by states via their COVID-19 dashboards and also provides an overview of \u201cbest practices\u201d states can consider when developing or modifying these same COVID-19 dashboards.<\/p>\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-4874\" src=\"https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-1-1024x312.png\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" srcset=\"https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-1-1024x312.png 1024w, https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-1-300x91.png 300w, https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-1-768x234.png 768w, https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-1.png 1114w\" alt=\"\" width=\"1024\" height=\"312\" \/><figcaption>Source: SHADAC COVID-19 dashboard template under development using mock data.<\/figcaption><\/figure>\n<h3 class=\"wp-block-heading\">Current Status of COVID-19 Dashboards<\/h3>\n<p>As of April 6, we identified 32 states with public-facing COVID-19 data dashboards (i.e., the information is displayed with charts and other graphics, not just in tabular form), and we anticipate that more states will publish COVID-19 dashboards in the coming days. States that currently publish COVID-19 dashboards include:<\/p>\n<figure class=\"wp-block-table\">\n<table>\n<tbody>\n<tr>\n<td><a href=\"https:\/\/alpublichealth.maps.arcgis.com\/apps\/opsdashboard\/index.html#\/6d2771faa9da4a2786a509d82c8cf0f7\">Alabama<\/a><\/td>\n<td><a href=\"https:\/\/kdhe.maps.arcgis.com\/apps\/opsdashboard\/index.html#\/05f4169dc6394aa98895072b94734134\">Kansas<\/a><\/td>\n<td><a href=\"https:\/\/www.health.nd.gov\/diseases-conditions\/coronavirus\/north-dakota-coronavirus-cases\">North Dakota<\/a><\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/coronavirus-response-alaska-dhss.hub.arcgis.com\/\">Alaska<\/a><\/td>\n<td><a href=\"http:\/\/ldh.la.gov\/Coronavirus\/\">Louisiana<\/a><\/td>\n<td><a href=\"https:\/\/coronavirus.ohio.gov\/wps\/portal\/gov\/covid-19\/home\/dashboard\">Ohio<\/a><\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/azdhs.gov\/preparedness\/epidemiology-disease-control\/infectious-disease-epidemiology\/index.php#novel-coronavirus-home\">Arizona<\/a><\/td>\n<td><a href=\"https:\/\/coronavirus.maryland.gov\/\">Maryland<\/a><\/td>\n<td><a href=\"https:\/\/public.tableau.com\/profile\/oregon.health.authority.covid.19#!\/vizhome\/OregonHealthAuthorityCOVID-19DataDashboard\/COVID-19EPIConfirmed?:display_count=y&amp;:toolbar=n&amp;:origin=viz_share_link&amp;:showShareOptions=false\">Oregon<\/a><\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/adem.maps.arcgis.com\/apps\/opsdashboard\/index.html#\/f533ac8a8b6040e5896b05b47b17a647\">Arkansas<\/a><\/td>\n<td><a href=\"https:\/\/www.michigan.gov\/coronavirus\/0,9753,7-406-98163_98173---,00.html\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\"Michigan (opens in a new tab)\">Michigan<\/a><\/td>\n<td><a href=\"https:\/\/www.scdhec.gov\/infectious-diseases\/viruses\/coronavirus-disease-2019-covid-19\/monitoring-testing-covid-19\">South Carolina<\/a><\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/public.tableau.com\/views\/COVID-19PublicDashboard\/Covid-19Public?:embed=y&amp;:display_count=no&amp;:showVizHome=no\">California<\/a><\/td>\n<td><a href=\"https:\/\/mndps.maps.arcgis.com\/apps\/opsdashboard\/index.html#\/f28f84968c1148129932c3bebb1d3a1a\">Minnesota<\/a><\/td>\n<td><a href=\"https:\/\/txdshs.maps.arcgis.com\/apps\/opsdashboard\/index.html#\/ed483ecd702b4298ab01e8b9cafc8b83\">Texas<\/a><\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/covid19.colorado.gov\/case-data\">Colorado<\/a><\/td>\n<td><a href=\"https:\/\/health.mo.gov\/living\/healthcondiseases\/communicable\/novel-coronavirus\/results.php\">Missouri<\/a><\/td>\n<td><a href=\"https:\/\/coronavirus.utah.gov\/case-counts\/\">Utah<\/a><\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/coronavirus.delaware.gov\/\">Delaware<\/a><\/td>\n<td><a href=\"https:\/\/montana.maps.arcgis.com\/apps\/MapSeries\/index.html?appid=7c34f3412536439491adcc2103421d4b\">Montana<\/a><\/td>\n<td><a href=\"http:\/\/www.vdh.virginia.gov\/coronavirus\/\">Virginia<\/a><\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/experience.arcgis.com\/experience\/96dd742462124fa0b38ddedb9b25e429\">Florida<\/a><\/td>\n<td><a href=\"https:\/\/nebraska.maps.arcgis.com\/apps\/opsdashboard\/index.html#\/4213f719a45647bc873ffb58783ffef3\">Nebraska<\/a><\/td>\n<td><a href=\"https:\/\/www.doh.wa.gov\/Emergencies\/Coronavirus\">Washington<\/a><\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/public.tableau.com\/profile\/idaho.division.of.public.health#!\/vizhome\/DPHIdahoCOVID-19Dashboard_V2\/DPHCOVID19Dashboard2\">Idaho<\/a><\/td>\n<td><a href=\"https:\/\/app.powerbigov.us\/view?r=eyJrIjoiMjA2ZThiOWUtM2FlNS00MGY5LWFmYjUtNmQwNTQ3Nzg5N2I2IiwidCI6ImU0YTM0MGU2LWI4OWUtNGU2OC04ZWFhLTE1NDRkMjcwMzk4MCJ9\">Nevada<\/a><\/td>\n<td><a href=\"https:\/\/dhhr.wv.gov\/COVID-19\/Pages\/default.aspx\">West Virginia<\/a><\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/coronavirus.in.gov\/\">Indiana<\/a><\/td>\n<td><a href=\"https:\/\/covid19.nj.gov\/#live-updates\">New Jersey<\/a><\/td>\n<td><a href=\"https:\/\/health.wyo.gov\/publichealth\/infectious-disease-epidemiology-unit\/disease\/novel-coronavirus\/covid-19-map-and-statistics\/\">Wyoming<\/a><\/td>\n<\/tr>\n<tr>\n<td><a href=\"https:\/\/coronavirus.iowa.gov\/\">Iowa<\/a><\/td>\n<td><a href=\"https:\/\/www.ncdhhs.gov\/divisions\/public-health\/covid19\/covid-19-nc-case-count\">North Carolina<\/a><\/td>\n<td><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p>States are reporting a wide number (ranging from 4 to 13) and type of indicators in their dashboards, most of which are updated at least daily. Many states are also starting to show trends in these data points over time. The most common indicators reported on a state dashboard include:<\/p>\n<ul>\n<li>Number of total cases<\/li>\n<li>Number of total deaths<\/li>\n<li>Number of cases by county<\/li>\n<li>Map of cases by county<\/li>\n<li>Number of tests completed<\/li>\n<li>Number of cases by age group<\/li>\n<li>Number of cases by gender<\/li>\n<li>Number of deaths by county<\/li>\n<li>Number of hospitalizations<\/li>\n<\/ul>\n<p>Other key indicators that some states are reporting that may be of interest include:<\/p>\n<ul>\n<li>Total number of recovered cases (i.e., cases<br \/>\nthat are no longer required to isolate)<\/li>\n<li>Number of hospitalizations that require<br \/>\nventilation<\/li>\n<li>Number of deaths by age\/gender\/race\/ethnicity<\/li>\n<li>Case rate per 100,000 people by county<\/li>\n<li>Number of cases by race\/ethnicity<\/li>\n<li>Number of cases by congregate living setting (e.g.,<br \/>\nlong-term care, assisted living, dorms, jails, correctional settings, etc.)<\/li>\n<li>Number of tests completed by laboratory type (e.g.,<br \/>\npublic vs. commercial labs)<\/li>\n<li>Number of tests completed by race\/ethnicity<\/li>\n<li>Number of calls to a state\u2019s COVID-19 hotline or<br \/>\nnumber of hits on a COVID-19 website<\/li>\n<\/ul>\n<p>It is important to note that states may be defining indicators that appear initially similar in different ways. For example, some states report \u201chospitalizations\u201d as the total number of cases who have ever been hospitalized, while other states report \u201chospitalizations\u201d as the current number of hospitalizations on a certain day. As a result, users should be cautious about making comparisons across states. In most cases, states have not identified the sources of their dashboard data beyond indicating that data is maintained by the respective state\u2019s health department (or equivalent) and that it comes from a variety of sources such as state and local public health surveillance data, lab data (including public health, hospital and commercial labs), and hospital reporting systems, among others. While the quality of the data being reported is difficult to assess currently (and is therefore reported as provisional), many states have acknowledged that data on confirmed cases represent an undercount due to a lack of widespread testing.\u00a0 Similarly, states have suggested that data on the number of deaths from COVID-19 may also change as post-mortem testing expands and guidance on how to record COVID-19 deaths is established.\u00a0 As mentioned below, we encourage states to include information on data quality, such as levels of missing data, where possible.<\/p>\n<h3 class=\"wp-block-heading\">COVID-19 Dashboard Best Practices<\/h3>\n<p><strong>Audience<\/strong>: Before designing a dashboard, make sure to clearly identify who is the intended audience. Different levels of detail, explanation, or source information may be necessary depending on whether the intended audience is state agency leadership, political leadership, or the general public. It is also important to think about what medium you will be using to reach the audience. Will the dashboard only be published on a website? Will it be available on a mobile device? Or, might you want to print it as a handout or post it on social media?<\/p>\n<p><strong>Organization and Layout<\/strong>: Prioritize key measures. Because timeliness of these data are so important, the dashboard needs to have enough data points to convey key information, but limited enough to update quickly. It is helpful to have a landing page that makes all indicators visible to users with limited scrolling, but also provides users with the ability to \u201cdrill down\u201d to more detail\u2014comparisons, methodology, etc. If it is not possible to show all indicators, there should be an obvious and intuitive option for the user to \u201chover\u201d over a list and get an \u201cat a glance\u201d view of the available content. The following example shows Florida\u2019s COVID-19 Dashboard landing page, which implements many of these best practices.<\/p>\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-4882\" src=\"https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-2.jpg\" sizes=\"auto, (max-width: 681px) 100vw, 681px\" srcset=\"https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-2.jpg 681w, https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-2-300x134.jpg 300w\" alt=\"\" width=\"681\" height=\"304\" \/><figcaption>Source: <a href=\"https:\/\/experience.arcgis.com\/experience\/96dd742462124fa0b38ddedb9b25e429\">Florida COVID-19 Data and Surveillance Dashboard<\/a>. Accessed March 30, 2020.<\/figcaption><\/figure>\n<p>Think about any potential layout in terms of a story. It is<br \/>\nhelpful to group indicators into high-level categories with headings (e.g.,<br \/>\noverview, demographics, hospitalizations, etc.). This provides additional<br \/>\ncontext for interpreting the data without the need for lengthy text<br \/>\ndescriptions. In addition, many dashboard are modular in nature so that visual<br \/>\nelements can be replaced as information relevancy changes over time (e.g.,<br \/>\ninformation on likely source of exposure may become less relevant over time,<br \/>\nwhile information on health care workforce exposure may become more relevant).<\/p>\n<p><strong>Health Equity<\/strong>: In order to understand the potentially disproportionate impact COVID-19 may have on communities of color, low-income, and other populations that face health disparities, it will be important for states to track both COVID-19 related cases and health outcome data disaggregated for key subpopulations, such as gender, race and ethnicity, geography (e.g., urban vs. rural) and insurance status. \u00a0Several states are reporting data by race\/ethnicity, which is critical as early reports suggest that the crisis is disproportionately impacting communities of color. For states that do report data on race, we recommend including detail about the scope of the missing data (and the reason, if possible) to help users interpret the findings. In the example below, North Carolina\u2019s dashboard reports confirmed cases and deaths broken down by race and ethnicity. North Carolina also clearly states the data\u2019s limitations \u2013 i.e., the number of cases for which race and ethnicity data are missing.<\/p>\n<p>Another breakdown important for monitoring equity, is<br \/>\ngeography.\u00a0 Nearly all states are<br \/>\nreporting data at the county level. It may also be helpful for states to<br \/>\npresent information that compares metrics in urban versus rural areas, as the<br \/>\nunique challenges of the virus (e.g., overcrowding in densely populated areas<br \/>\nvs. more limited hospital resources in rural areas) differ significantly by<br \/>\nthese factors. There are several approaches to defining <a href=\"https:\/\/www2.census.gov\/geo\/pdfs\/reference\/ua\/Defining_Rural.pdf\">urban versus<br \/>\nrural<\/a> areas and each have advantages and disadvantages, but given that<br \/>\nstates are already collecting COVID-19 information at the county level, it may<br \/>\nbe most straightforward to disaggregate information using the Census definition<br \/>\nthat classifies counties as \u201ccompletely rural\u201d, \u201cmostly rural\u201d, and \u201cmostly<br \/>\nurban\u201d.<\/p>\n<p>In addition to providing data by race and geography, it<br \/>\nwould be ideal if states could provide additional subpopulation breakdowns such<br \/>\nas primary language, socioeconomic status (e.g., education, income, occupation),<br \/>\nand disability status, if the data are available. Due to the rapid emergency<br \/>\nresponse required to address the COVID-19 outbreak, we realize states may not<br \/>\ninitially have the time or bandwidth to produce a broad range of subpopulation<br \/>\nanalysis or to conduct additional analyses of their demographic data, such as<br \/>\nlooking at the intersectionality of data (e.g., by race and gender). However,<br \/>\nthose types of analyses will be increasingly important as states seek to<br \/>\nunderstand disparities in COVID-19 treatment access, morbidity and mortality.<\/p>\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-4886\" src=\"https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-3-905x1024.jpg\" sizes=\"auto, (max-width: 905px) 100vw, 905px\" srcset=\"https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-3-905x1024.jpg 905w, https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-3-265x300.jpg 265w, https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-3-768x869.jpg 768w, https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-3-1357x1536.jpg 1357w, https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-3.jpg 1726w\" alt=\"\" width=\"905\" height=\"1024\" \/><figcaption>Source: COVID-19 North Carolina Dashboard. \u00a0Accessed April 6, 2020.<\/figcaption><\/figure>\n<p><strong>Date and Time-Stamps<\/strong>: Because these indicators are subject to change so rapidly, it will be important to date and time-stamp any dashboard updates. In addition to date-stamping the entire dashboard, also consider adding the date (and source information) to any graphic that could potentially be used a stand-alone item in another report or on social media. For example, the graphic below represents the age distribution of a state\u2019s COVID-19 cases and is labeled \u201cAs of 3\/31\/2020\u201d so that it\u2019s clear what time period this represents, even when the image is viewed separately from the overall dashboard.<\/p>\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-4892\" src=\"https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-4.jpg\" sizes=\"auto, (max-width: 826px) 100vw, 826px\" srcset=\"https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-4.jpg 826w, https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-4-300x112.jpg 300w, https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-4-768x286.jpg 768w\" alt=\"\" width=\"826\" height=\"308\" \/><figcaption>Source: SHADAC COVID-19 dashboard template under development using mock data.<\/figcaption><\/figure>\n<p><strong>Data Labels, Definitions, and Sources<\/strong>: Provide clear data labels and documentation. Although you should avoid \u201ccluttering\u201d a dashboard with extensive text, it is also important to provide the audience with information about data definitions and sources. Below is an example from North Dakota\u2019s data dashboard showing how they included definitions for of each of their six key indicators below their visualization. If space is limited, it is fine to provide hyperlinks to more detailed information on these factors. However, the links should be tested regularly to ensure they are still \u201clive\u201d and taking users to the correct information.<\/p>\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-4896\" src=\"https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-5.jpg\" sizes=\"auto, (max-width: 660px) 100vw, 660px\" srcset=\"https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-5.jpg 660w, https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-5-300x217.jpg 300w\" alt=\"\" width=\"660\" height=\"478\" \/><figcaption>Source: <a href=\"https:\/\/www.health.nd.gov\/diseases-conditions\/coronavirus\/north-dakota-coronavirus-cases\">North Dakota COVID-19 Dashboard<\/a>. Accessed March 30, 2020.<\/figcaption><\/figure>\n<p><strong>Time-Series Data<\/strong>: Visually displaying time-series data is an effective way to track changes. In order to improve readability, try to ensure that all time-trended data on the dashboard starts with the same date and covers the same time period, if possible. For example, although deaths and hospitalizations began ramping up at different times, these two time-trended graphs on Ohio\u2019s dashboard start on the same date and cover the same time period. States may also choose to have a dual axis marking both the date and the week (as shown in the first figure at the top of the page). This helps users understand the broader context of the trends being displayed.<\/p>\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-4898\" src=\"https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-6.jpg\" sizes=\"auto, (max-width: 444px) 100vw, 444px\" srcset=\"https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-6.jpg 444w, https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-6-218x300.jpg 218w\" alt=\"\" width=\"444\" height=\"610\" \/><figcaption>Source: <a href=\"https:\/\/coronavirus.ohio.gov\/wps\/portal\/gov\/covid-19\/home\/dashboard\">State of Ohio COVD-19 Dashboard<\/a>. Accessed March 30, 2020.<\/figcaption><\/figure>\n<p><strong>Visualizations<\/strong>: Choose visualizations that are clean and compliant with a range of browsers. Simple visualizations can also help users interpret more complex data \u201cat a glance.\u201d For example, many dashboards use up or down arrows to indicate whether most recent data show improvements or declines. Make sure visualizations require limited manual data manipulation. For example, the visual below was created so that it links to a back-end Excel spreadsheet, which is easily refreshed.<\/p>\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-4901\" src=\"https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-7.jpg\" sizes=\"auto, (max-width: 641px) 100vw, 641px\" srcset=\"https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-7.jpg 641w, https:\/\/shvs.org\/wp-content\/uploads\/2020\/04\/Picture-7-300x142.jpg 300w\" alt=\"\" width=\"641\" height=\"303\" \/><figcaption>Source: SHADAC COVID-19 dashboard template under development using mock data.<\/figcaption><\/figure>\n<p><strong>Documentation to Support Data Updates<\/strong>: After constructing your customized dashboard, create an \u201cinstruction sheet\u201d outlining all of the steps necessary to update the data on an ongoing basis, including:<\/p>\n<ul>\n<li>Which specific cells or inputs need daily updates<\/li>\n<li>What data sources are being used and where the<br \/>\ndata is located<\/li>\n<li>How and where to document what data was pulled<br \/>\nand when<\/li>\n<\/ul>\n<p>This detailed \u201cinstruction sheet\u201d is especially important in<br \/>\nthe event that the individual who normally updates the data is absent or<br \/>\nleaves\u2014that way someone else can easily complete the update.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Emily Zylla and Lacey Hartman, SHADAC Accurate, timely data is a key tool in states\u2019 efforts to understand and respond to the impact of the coronavirus (COVID-19) outbreak at the local level. There have also been increasing calls to further break down COVID-10 data into subcategories (such as by gender, age, and race and ethnicity) [&hellip;]<\/p>\n","protected":false},"author":24,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[1],"tags":[2],"class_list":["post-4870","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-shvs"],"acf":[],"_links":{"self":[{"href":"https:\/\/shvs.org\/wp-json\/wp\/v2\/posts\/4870","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/shvs.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/shvs.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/shvs.org\/wp-json\/wp\/v2\/users\/24"}],"replies":[{"embeddable":true,"href":"https:\/\/shvs.org\/wp-json\/wp\/v2\/comments?post=4870"}],"version-history":[{"count":18,"href":"https:\/\/shvs.org\/wp-json\/wp\/v2\/posts\/4870\/revisions"}],"predecessor-version":[{"id":13380,"href":"https:\/\/shvs.org\/wp-json\/wp\/v2\/posts\/4870\/revisions\/13380"}],"wp:attachment":[{"href":"https:\/\/shvs.org\/wp-json\/wp\/v2\/media?parent=4870"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/shvs.org\/wp-json\/wp\/v2\/categories?post=4870"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/shvs.org\/wp-json\/wp\/v2\/tags?post=4870"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}