In March 2020, it was estimated that the infection rate for COVID-19 ranged between 1.5 and 3.5. In comparison, the seasonal flu had an infection rate of 1.3. Data is subject to change due to the developing situation with the coronavirus pandemic.

Rising infection rates could reignite virus COVID-19 is an infectious disease that continues to threaten different parts of the world simultaneously. The number of positive cases in the United States topped 5.5 million on August 22, 2020, and the potential for new waves of infection remains. In several U.S. states, the infection rate is higher than one, which means each infected person is passing the virus to more than one other person. When an infection rate is less than one, the outbreak will weaken because the viral pathogen is not as widely spread.

The importance of isolation Someone who has been diagnosed with COVID-19 can easily spread the virus to others. For this reason, patients are urged to self-isolate for around 14 days. To further reduce the risk of transmission, people who have been in close contact with a positive case should also self-isolate, even if they feel healthy. National testing programs make it easier to track the spread of the virus and are helping to flatten the infection curve. The U.S. had conducted more than 70 million coronavirus tests as of August 24, 2020 – the states of California and New York had performed more than any other.

As of January 23, 2021, Vermont had the highest Rt value of any U.S. state. The Rt value indicates the average number of people that one person with COVID-19 is expected to infect. A number higher than one means each infected person is passing the virus to more than one other person.

Which are the hardest-hit states? The U.S. reported its first confirmed coronavirus case toward the end of January 2020. More than 28 million positive cases have since been recorded as of February 24, 2021 – California and Texas are the states with the highest number of coronavirus cases in the United States. When figures are adjusted to reflect each state’s population, North Dakota has the highest rate of coronavirus cases. The vaccine rollout has provided Americans with a significant morale boost, and California is the state with the highest number of COVID-19 vaccine doses administered.

How have other nations responded? Countries around the world have responded to the pandemic in varied ways. The United Kingdom has approved three vaccines for emergency use and ranks among the countries with the highest number of COVID-19 vaccine doses administered worldwide. In the Asia-Pacific region, the outbreak has been brought under control in New Zealand, and the country’s response to the pandemic has been widely praised.

These data contain case counts and rates for selected communicable diseases—listed in the data dictionary—that met the surveillance case definition for that disease and was reported for California residents, by disease, county, year, and sex. The data represent cases with an estimated illness onset date from 2001 through the last year indicated from California Confidential Morbidity Reports and/or Laboratory Reports. Data captured represent reportable case counts as of the date indicated in the “Temporal Coverage” section below, so the data presented may differ from previous publications due to delays inherent to case reporting, laboratory reporting, and epidemiologic investigation.

Human Immunodeficiency Virus (HIV) remains a significant public health concern, with adults being at greater risk. Thus, understanding the dynamics of HIV transmission is crucial for effective prevention and control strategies, hence the need for a continuous clinical survey of the patients’ records of diagnosis and treatment for HIV. The data include the quarterly records of 138 adults diagnosed with HIV at Osun State University Teaching Hospital, Nigeria which involves the number of adults tested positive and negative for each of the endogenous variables discussed below. Information was sought using a convenient sampling method, which entails careful selection of individual records based on availability. The data was grouped into quarterly records of the diagnosed adults, with an average age ranging between 26 years and 52 years, and spread between the years 2008 and 2021. The records comprise 72 Females and 66 Males while the presence of each symptom is coded as 1 and the absence coded as 0. The endogenous variables observed in the clinical records of the surveyed patients are Fever (F), Diarrhea (D), Abdominal pain (AP), Skin rash (SR), Mouth sour (MS), Cellulitis (C), Coughing with sputum (CS), Loss of appetite (LA), Genital infections (GI), Medical fitness (MF), Headache (H), Catarrh (CA), Weight Loss (WL), Excessive Sweat (ES), Mouth Sour (MS), and Body weakness (BW). The impacts of these aforementioned factors would be examined on the spread of HIV. The clinical survey revealed that 77 individuals (55.80%) did not experience fever, while 61 (44.20%) did. Diarrhea was reported by 39 participants (28.26%), leaving 99 (71.74%) without this symptom. Abdominal pain and cellulitis were both reported by only 4 individuals (2.90%), with 134 participants (97.10%) indicating no occurrences of these symptoms. In terms of medical fitness, 110 individuals (79.71%) reported no fitness issues, whereas 28 (20.29%) reported having some. Cough with sputum affected 50 participants (36.23%), while 88 (63.77%) did not report this symptom. Headaches were almost universally absent, with 137 individuals (99.28%) not experiencing any. Catarrh was present in 14 participants (10.14%), with 124 (89.86%) reporting no instances. Loss of appetite was reported by 5 individuals (3.62%), and skin rashes were observed in 28 participants (20.29%). Weight loss affected 49 individuals (35.51%), and excessive sweating was reported by 137 participants (99.28%). Mouth soreness was noted in 27 participants (19.57%), while genital infections were reported by 6 individuals (4.35%). Body weakness was reported by 49 participants (35.51%). In the age distribution, 56 individuals (40.58%) fall into the young adult’s category while 82 individuals (59.42%) are categorized as older adults. Notably, all participants in the study were confirmed to be HIV positive, emphasizing a focused analysis of this group’s health characteristics.

The following dashboards provide data on contagious respiratory viruses, including acute respiratory diseases, COVID-19, influenza (flu), and respiratory syncytial virus (RSV) in Massachusetts. The data presented here can help track trends in respiratory disease and vaccination activity across Massachusetts.

In terms of the number of infected people, the novel coronavirus (SARS-CoV-2) ranked third among ten major virus outbreaks as of the end of January 2020. The virus, which originated from the Chinese city of Wuhan, has since spread to around 215 countries and territories worldwide.

China searching for disease’s origins The cumulative number of COVID-19 cases in China topped 89,000 on August 11, 2020. The SARS-CoV-2 virus and the infectious disease it causes were unknown before the outbreak began in China in December 2019. Experts from the World Health Organization are now working with Chinese counterparts to identify the origins of the virus. The most common symptoms reported by Chinese patients were fever, dry cough, and fatigue.

The rapid global spread of the virus In March 2020, it was estimated that the SARS-CoV-2 virus had an infection rate of between 1.5 and 3.5, which is higher than other outbreaks that have emerged worldwide in the past two decades. According to early estimates in January 2020, the case fatality rate was around two percent, but the spread of the coronavirus has overwhelmed many countries. The case fatality rate in China was as high as 5.5 percent in mid-April 2020.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Data is from the California Department of Public Health (CDPH) Respiratory Virus Weekly Report.

The report is updated each Friday.

Laboratory surveillance data: California laboratories report SARS-CoV-2 test results to CDPH through electronic laboratory reporting. Los Angeles County SARS-CoV-2 lab data has a 7-day reporting lag. Test positivity is calculated using SARS-CoV-2 lab tests that has a specimen collection date reported during a given week.

Laboratory surveillance for influenza, respiratory syncytial virus (RSV), and other respiratory viruses (parainfluenza types 1-4, human metapneumovirus, non-SARS-CoV-2 coronaviruses, adenovirus, enterovirus/rhinovirus) involves the use of data from clinical sentinel laboratories (hospital, academic or private) located throughout California. Specimens for testing are collected from patients in healthcare settings and do not reflect all testing for influenza, respiratory syncytial virus, and other respiratory viruses in California. These laboratories report the number of laboratory-confirmed influenza, respiratory syncytial virus, and other respiratory virus detections and isolations, and the total number of specimens tested by virus type on a weekly basis.

Test positivity for a given week is calculated by dividing the number of positive COVID-19, influenza, RSV, or other respiratory virus results by the total number of specimens tested for that virus. Weekly laboratory surveillance data are defined as Sunday through Saturday.

Hospitalization data: Data on COVID-19 and influenza hospital admissions are from Centers for Disease Control and Prevention’s (CDC) National Healthcare Safety Network (NHSN) Hospitalization dataset. The requirement to report COVID-19 and influenza-associated hospitalizations was effective November 1, 2024. CDPH pulls NHSN data from the CDC on the Wednesday prior to the publication of the report. Results may differ depending on which day data are pulled. Admission rates are calculated using population estimates from the P-3: Complete State and County Projections Dataset provided by the State of California Department of Finance (https://dof.ca.gov/forecasting/demographics/projections/). Reported weekly admission rates for the entire season use the population estimates for the year the season started. For more information on NHSN data including the protocol and data collection information, see the CDC NHSN webpage (https://www.cdc.gov/nhsn/index.html).

CDPH collaborates with Northern California Kaiser Permanente (NCKP) to monitor trends in RSV admissions. The percentage of RSV admissions is calculated by dividing the number of RSV-related admissions by the total number of admissions during the same period. Admissions for pregnancy, labor and delivery, birth, and outpatient procedures are not included in total number of admissions. These admissions serve as a proxy for RSV activity and do not necessarily represent laboratory confirmed hospitalizations for RSV infections; NCKP members are not representative of all Californians.

Weekly hospitalization data are defined as Sunday through Saturday.

Death certificate data: CDPH receives weekly year-to-date dynamic data on deaths occurring in California from the CDPH Center for Health Statistics and Informatics. These data are limited to deaths occurring among California residents and are analyzed to identify influenza, respiratory syncytial virus, and COVID-19-coded deaths. These deaths are not necessarily laboratory-confirmed and are an underestimate of all influenza, respiratory syncytial virus, and COVID-19-associated deaths in California. Weekly death data are defined as Sunday through Saturday.

Wastewater data: This dataset represents statewide weekly SARS-CoV-2 wastewater summary values. SARS-CoV-2 wastewater concentrations from all sites in California are combined into a single, statewide, unit-less summary value for each week, using a method for data transformation and aggregation developed by the CDC National Wastewater Surveillance System (NWSS). Please see the CDC NWSS data methods page for a description of how these summary values are calculated. Weekly wastewater data are defined as Sunday through Saturday.

A. SUMMARY This dataset includes weekly respiratory disease hospital admissions for Influenza, RSV, and COVID-19 into San Francisco hospitals. Columns in the dataset include a count and rate of hospital admissions per 100,000 people. The data are reported by week. B. HOW THE DATASET IS CREATED Hospital admission data is reported to the San Francisco Department of Public Health (SFDPH) from the United States Center for Disease Control’s (CDC) National Healthcare Safety Network (NHSN) program. San Francisco population estimates are pulled from a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2019-2023 5-year American Community Survey (ACS). C. UPDATE PROCESS The dataset is updated every Friday and includes data from the previous Sunday through Saturday. For example, the update on Friday, October 17th will include data through Saturday, October 11th. Data may change as more current information becomes available. D. HOW TO USE THIS DATASET Weekly data represent a count of confirmed admissions of Influenza, RSV, and COVID-19 patients to San Francisco hospitals by week. The admission rate per 100,000 is calculated by multiplying the count of admissions each week by 100,000 and dividing by the population estimate.

For each variable we give its unit, the standard deviation (SD) of the input time series and the percent change with its weighted SD of the transmission rate associated with a unit SD change in the input.

This image reports a Maximum Entropy model that estimates suitable locations for COVID-19 spread, i.e. places that could favour the spread of the virus just in terms of environmental parameters.

The model was trained just on locations in Italy that have reported a rate of new infections higher than the geometric mean of all Italian infection rates. The following environmental parameters were used, which are correlated to those used by other studies:

• Average Annual Surface Air Temperature in 2018 (NASA)
• Average Annual Precipitation in 2018 (NASA)
• CO2 emission (natural+artificial) averaged between January 1979 and December 2013 (Copernicus Atmosphere Monitoring Service)
• Elevation (NOAA ETOPO2)

A higher resolution map, the model file (in ASC format) and all parameters used are also attached.

The model indicates highest correlation to infection rate for CO2 around 0.03 gCm^−2day^−1, for Temperature around 11.8 °C, and for Precipitation around 0.3 kg m^-2 s^-1, whereas Elevation is poorly correlated.

One interesting result is that the model indicates, among others, the Hubei region in China as a high-probability location, and Iran (around Teheran) as a suited location for virus' spread, but the model was not trained on these regions, i.e. it did not know about the actual spread in these regions.

Note: On April 30, 2024, the Federal mandate for COVID-19 and influenza associated hospitalization data to be reported to CDC’s National Healthcare Safety Network (NHSN) expired. Hospitalization data beyond April 30, 2024, will not be updated on the Open Data Portal. Hospitalization and ICU admission data collected from summer 2020 to May 10, 2023, are sourced from the California Hospital Association (CHA) Survey. Data collected on or after May 11, 2023, are sourced from CDC's National Healthcare Safety Network (NHSN).

Data is from the California Department of Public Health (CDPH) Respiratory Virus State Dashboard at https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/Respiratory-Viruses/RespiratoryDashboard.aspx.

Data are updated each Friday around 2 pm.

For COVID-19 death data: As of January 1, 2023, data was sourced from the California Department of Public Health, California Comprehensive Death File (Dynamic), 2023–Present. Prior to January 1, 2023, death data was sourced from the COVID-19 case registry. The change in data source occurred in July 2023 and was applied retroactively to all 2023 data to provide a consistent source of death data for the year of 2023. Influenza death data was sourced from the California Department of Public Health, California Comprehensive Death File (Dynamic), 2020–Present.

COVID-19 testing data represent data received by CDPH through electronic laboratory reporting of test results for COVID-19 among residents of California. Testing date is the date the test was administered, and tests have a 1-day lag (except for the Los Angeles County, which has an additional 7-day lag). Influenza testing data represent data received by CDPH from clinical sentinel laboratories in California. These laboratories report the aggregate number of laboratory-confirmed influenza virus detections and total tests performed on a weekly basis. These data do not represent all influenza testing occurring in California and are available only at the state level.

Infection, dissemination and transmission rates in seven Australian mosquito species exposed to a blood meal containing 106.7 ± 0.2 TCID50/mL of ZIKV.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

This dataset is associated with the publication "G.Coro, (2020), A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate, Ecological Modelling, Volume 431, 109187, https://doi.org/10.1016/j.ecolmodel.2020.109187"

This image reports a Maximum Entropy model that estimates suitable locations for COVID-19 spread, i.e. places that could favour the spread of the virus just in terms of environmental parameters.

The model was trained just on locations in Italy that have reported a rate of new infections higher than the geometric mean of all Italian infection rates. The following environmental parameters were used, which are correlated to those used by other studies:

• Average Annual Surface Air Temperature in 2018 (NASA)
• Average Annual Precipitation in 2018 (NASA)
• CO2 emission (natural+artificial) averaged between January 1979 and December 2013 (Copernicus Atmosphere Monitoring Service)
• Elevation (NOAA ETOPO2)
• Population per 0.5° cell (NASA Gridded Population of the World)

A higher resolution map, the model file (in ASC format) and all parameters used are also attached.

The model indicates highest correlation with infection rate for CO2 around 0.03 gCm^−2day^−1, for Temperature around 11.8 °C, and for Precipitation around 0.3 kg m^-2 s^-1, whereas Elevation and Population density are poorly correlated with infection rate.

One interesting result is that the model indicates, among others, the Hubei region in China as a high-probability location, and Iran (around Teheran) as a suited location for virus' spread, but the model was not trained on these regions, i.e. it did not know about the actual spread in these regions.

Evaluation:

A risk score was calculated for each country/region reported by the JHU monitoring system (https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6). This score is calculated as the summed normalised probability in the populated locations divided by their total surface. This score represents how much the zone would potentially foster the virus' spread.

We assessed the reliability of this score, by selecting the country/regions that reported the highest rates of infection. These zones were selected as those with a rate higher than the upper confidence of a log-normal distribution of the rates.

The agreement between the two maps (covid_high_rate_vs_high_risk.png, where violet dots indicate high infection rates and countries' colours indicate estimated high risk score) is the following:

Accuracy (overall percentage of correctly predicted high-rate zones): 77.25%
Kappa (agreement between the two maps): 0.46 (Good, according to Fleiss' intepretation of the score)

This assessment demonstrates that our map can be used to estimate the risk of a certain country to have a high rate of infection, and indicates that the influence of environmental parameters on virus's spread should be further investigated.

BackgroundConventional epidemiological surveillance of infectious diseases is focused on characterization of incident infections and estimation of the number of prevalent infections. Advances in methods for the analysis of the population-level genetic variation of viruses can potentially provide information about donors, not just recipients, of infection. Genetic sequences from many viruses are increasingly abundant, especially HIV, which is routinely sequenced for surveillance of drug resistance mutations. We conducted a phylodynamic analysis of HIV genetic sequence data and surveillance data from a US population of men who have sex with men (MSM) and estimated incidence and transmission rates by stage of infection.Methods and FindingsWe analyzed 662 HIV-1 subtype B sequences collected between October 14, 2004, and February 24, 2012, from MSM in the Detroit metropolitan area, Michigan. These sequences were cross-referenced with a database of 30,200 patients diagnosed with HIV infection in the state of Michigan, which includes clinical information that is informative about the recency of infection at the time of diagnosis. These data were analyzed using recently developed population genetic methods that have enabled the estimation of transmission rates from the population-level genetic diversity of the virus. We found that genetic data are highly informative about HIV donors in ways that standard surveillance data are not. Genetic data are especially informative about the stage of infection of donors at the point of transmission. We estimate that 44.7% (95% CI, 42.2%–46.4%) of transmissions occur during the first year of infection.ConclusionsIn this study, almost half of transmissions occurred within the first year of HIV infection in MSM. Our conclusions may be sensitive to un-modeled intra-host evolutionary dynamics, un-modeled sexual risk behavior, and uncertainty in the stage of infected hosts at the time of sampling. The intensity of transmission during early infection may have significance for public health interventions based on early treatment of newly diagnosed individuals.Please see later in the article for the Editors' Summary

Pathogen spread rates are determined, in part, by the performance of pathogens under altered environmental conditions and their ability to persist while switching among hosts and vectors.

To determine the effects of new conditions (host, vector, and nutrient) on pathogen spread rate, we introduced a vector-borne, viral plant pathogen, Barley Yellow Dwarf Virus PAV (BYDV-PAV) into hosts, vectors, and host nutrient supplies that it had not encountered for thousands of viral generations. We quantified pathogen prevalence over the course of two serial inoculations under the new conditions. Using individual level transmission rates from this experiment, we parameterized a dynamical model of disease spread and projected spread across host populations through a growing season.

A change in nutrient conditions (increased supply of phosphorus) reduced viral transmission whereas shifting to a new vector or host species had no effect on infection prevalence. However, the reduction in the new nutrient environment was only temporary; infection prevalence recovered after the second inoculation.

Synthesis. These results highlight how robust the pathogen, BYDV-PAV, is to changes in its biotic and abiotic environment. Our study also highlights the need to quantify longitudinal infection information beyond snapshot assessments to project disease risk for pathogens in new environments.

Methods Natal conditions

Our experiment used BYDV-PAV viral cultures that had been maintained in the laboratory (see Viral Culture Source in Appendix) using a single aphid species, S. avenae, on a single host species, A. sativa, under low nutrient conditions for 251 days (see detailed information regarding vector colony and host plant source in Appendix, Vector Conditions and Host Conditions). Aphid population growth under these natal conditions produces approximately 12 generations of S. avenae (Dedryver et al., 1998), and approximately 1,800 to 6,000 generations of BYDV-PAV (Yarwood, 1956).

Treatment conditions

We experimentally exposed natal viral cultures to a range of conditions consisting of two aphid vectors, two plant hosts, and four nutrient conditions for a total of 16 treatments. The treatments consisted of a full cross of vector (two conditions), host (two conditions), and nutrient (four conditions) across the natal, S. avenae, or new aphid vector, R. padi, the natal, A. sativa, or new host species, H. vulgare, and the nutrient conditions. The nutrient conditions included nitrogen (NH4NO3), phosphorus (KH2PO4), nitrogen plus phosphorus, or no additional nutrients. Each treatment was repeated eight times over three consecutive temporal blocks for a total 24 replicates per treatment. For each block of the experiment, 70 seeds from each host plant species were planted for a total of 140 plants. Each block had eight replicates per treatment with 12 additional seeds planted to account for the possibility of for failed germinations. The plants were watered with the four nutrient treatments: nanopure water only (Control), 10% nitrogen solution, 10% phosphorus solution, or 10% nitrogen & phosphorus solution all based on a Half-Strength Hoagland’s solution (Hoagland and Arnon 1950) which are consistent with previous experiments (Lacroix, Seabloom and Borer, 2014). The first inoculation that introduced the virus to new biotic and abiotic conditions will be referred to as Round 1. We then performed a second inoculation (referred to as Round 2) in vivo such that all treatments were applied to a set of hosts that maintained the treatment where plant tissue from Round 1 treatments was used to infect the aphids used in Round 2. A simple schematic depicting the inoculation and treatment conditions is represented in Fig. 1. Assessing viral evolution and population dynamics after serial passages is not uncommon (Sylvester, Richardson and Frazier, 1974; Kurath and Palukaitis, 1990; Schneider and Roossinck, 2000; Bartels et al., 2016) but quantifying virus transmission in serially passaged viruses after switching abiotic or biotic conditions has rarely been performed. All plant tissues were collected between June 5th, 2017 and October 10, 2017. All plant tissue was collected and preserved at -20C for molecular processing.

Virus inoculation

Each block as described under Treatment Conditions, included two inoculation rounds. During the first inoculation (Round 1) of the experiment, 360 live, adult-sized aphids of both R. padi and S. avenae were removed from uninfected plants and transferred to 25ml cork sealed tubes (24x) each containing 30 aphids of the same species. Leaf tissue from approximately four-week-old plants confirmed to be infected with BYDV-PAV was clipped and 4-6 cm of infected tissue was transferred into each tube containing non-viruliferous aphids. Aphids remained in cork sealed tubes for 48 hours such that they became viruliferous from feeding on infected plant tissue, meaning the aphids were then able to transmit the virus. After 48 hours, aphids were moved to uninfected plants for the initial inoculation period. Plants used for the initial inoculation were uninfected prior to aphid exposure as the plants remained isolated from aphids and other insects and there is no evidence of vertical transmission of BYDV-PAV in hosts.

We controlled for factors known to influence transmission efficiency including length of feeding period on infected tissue and age of host tissue (Gray et al., 1991). To do this, a single 2.5 x 8.5 cm, 118 μm polyester mesh cage was attached to the oldest leaf on each 17-day old experimental plant. Five viruliferous aphids were transferred into each polyester mesh cage which was then sealed. The experimental plants containing the caged aphids were then placed in a growth chamber and aphids fed for approximately 96 hours, after which the aphids were killed to end transmission.

At the start of the second inoculation (Round 2), the experimental plants from Round 1 were destructively harvested and the polyester mesh cages were removed. Each 8.5 cm leaf enclosed by the cage was cut from the plant and transferred to a clean 25 ml tube while the above-ground plant tissue was stored at -20C. Once all tissues were collected at the end of the experiment, BYDV-PAV infection status (presence/absence) was assessed using polymerase chain reaction (see Virus detection in Appendix). Ten aviruliferous aphids (i.e., not yet carrying a virus) of each species were collected from vector source conditions, transferred into each of the tubes respective to the treatment, and allowed to feed for 48-hours. Five aphids from each tube were transferred to a new experimental plant raised under the treatment conditions and cage-sealed; the remaining five aphids were discarded. During Round 2, the treatments with R. padi in the sixth block only contained one aphid per cage due to R. padi colony depletion. All other treatments contained five aphids per cage per block. The experimental plants were subjected to feeding period of 96 hours after which the aphids were killed. BYDV-PAV infection status (presence/absence) was assessed using polymerase chain reaction (see Virus detection in Appendix).

Actions of Public Health France Public Health France’s mission is to improve and protect the health of populations. During the health crisis linked to the COVID-19 outbreak, Santé publique France is responsible for monitoring and understanding the dynamics of the epidemic, anticipating the various scenarios and putting in place actions to prevent and limit the transmission of this virus on national territory. ### The Tracking Information System (SI-DEP) The new screening information system (SI-DEP), which has been in operation since 13 May 2020, is a secure platform where the results of the laboratory tests carried out by all city and hospital laboratories for SARS-COV2 are systematically recorded. The creation of this information system is authorised for a period of 6 months from the end of the state of health emergency by application of Decree No 2020-551 of 12 May 2020 on the information systems referred to in Article 11 of Law No 2020-546 of 11 May 2020 extending the state of health emergency and supplementing its provisions. ### Description of data This dataset provides information at the departmental and regional level: — the daily and weekly incidence rate per age group; — the daily and weekly standardised incidence rate; — the sliding standardised incidence rate. This dataset provides information at the national level: — the daily and weekly incidence rate by age group and sex; — the daily and weekly standardised incidence rate; — the sliding standardised incidence rate. The incidence rate corresponds to the number of positive tests per 100,000 inhabitants. It shall be calculated as follows: (100000 * number of positive cases)/Population Accuracy: — From 29/08 onwards, laboratory data indicators (SI-DEP) show rates of incidence, positivity and screening adjusted for screenings conducted at airports upon arrival of international flights. — For more information, see the methodological note available in the resources. Limits: — Only the biological tests of persons for whom the residence department could be located are shown on the maps. Persons whose department could not be traced in the SIDEP data are counted only at the whole French level. As a result, the sum of the tests indicated in the departments or regions is less than the number of tests indicated in France. — The time limit for repeating tests may exceed 9 days in some cases. The indicators are adjusted daily according to the receipt of the results. ### Notable changes Since 8 December, after verifying the quality of the reported data, all results of RT-PCR or Antigenic tests have been included in the production of national and territorial epidemiological indicators (incidence rates, positivity rates and screening rates) relevant to the monitoring of the COVID-19 outbreak. On the other hand, the epidemic is prolonging in time and screening capacities have increased, leading to an increasing frequency of people tested several times. Thus, an adjustment of the methods of splitting for patients benefiting from repeated tests and therefore the definition of the persons tested was necessary. Public Health France, in its patient-centred epidemiological approach, has therefore adapted its methods to ensure that these indicators reflect, in particular, the proportion of infected people among the population tested. These developments have no impact on the trends and interpretation of the dynamics of the epidemic, which remain the same. More precise test data (impact and positivity) are also published by Santé publique France (SI-DEP data).

As of March 10, 2023, the state with the highest rate of COVID-19 cases was Rhode Island followed by Alaska. Around 103.9 million cases have been reported across the United States, with the states of California, Texas, and Florida reporting the highest numbers of infections.

From an epidemic to a pandemic The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. The term pandemic refers to multiple outbreaks of an infectious illness threatening multiple parts of the world at the same time; when the transmission is this widespread, it can no longer be traced back to the country where it originated. The number of COVID-19 cases worldwide is roughly 683 million, and it has affected almost every country in the world.

The symptoms and those who are most at risk Most people who contract the virus will suffer only mild symptoms, such as a cough, a cold, or a high temperature. However, in more severe cases, the infection can cause breathing difficulties and even pneumonia. Those at higher risk include older persons and people with pre-existing medical conditions, including diabetes, heart disease, and lung disease. Those aged 85 years and older have accounted for around 27 percent of all COVID deaths in the United States, although this age group makes up just two percent of the total population

This is the first systematic review and meta-analysis summarizing the current evidence related to the cost of RSV disease among children below 5 years of age. It demonstrates that the economic burden associated with RSV disease is substantial. All data variables in the spreadsheet are documented in the publication, either in the main test or supplementary material.