The country’s first “gender-sensitive” online dashboard for COVID-19 lets users break down datasets into either male or female (or both) to render sex-disaggregated epidemic curve and graphs and graphs. Users can easily crunch data according to key variables as health status, age group, location from regional to the municipal level, and a specific date range, among other useful features for more nuanced insights on the COVID-19 pandemic data. (Photo: A screengrab of the dashboard’s main user interface)
Luzon, Philippines – The country’s first “gender-sensitive” online dashboard for COVID-19 was unveiled by the National Research Council of the Philippines - Department of Science and Technology (DOST-NRCP) through a Zoom webinar hosted by the NRCP last Tuesday, 7 July 2020. The dashboard is unique among other existing local COVID-19 dashboards in breaking down datasets into sex-disaggregated graphs for more nuanced insights to aid decision-makers, government administrators, academics, and the general public.
NRCP Governing Board Member-at-large and University of the Philippines Los Baños (UPLB) Mathematics Professor, Dr. Jomar F. Rabajante, gave a walk-through of the dashboard’s key features in a webinar billed “KTOP-COVID,” short for “Kapakanan ng Tao sa Oras ng Pandemya - COVID.” His featured study is second in a webinar series highlighting the results of the Council’s funded short-term researches that address the various social dimensions related to the COVID-19 pandemic.
The dashboard was specifically designed for extracting sex-specific insights by disaggregating datasets per sex (male or female), integrating sex-specific epidemiological information to available social economic data, and rendering them into various colorful visualizations or useful “story boards” to promote evidence-based decision making.
Rabajante emphasized that more than an epidemiological or health issue, the pandemic has become a more pressing social and economic issue per the extent of its impacts across age, sex, and socioeconomic groups. He said interventions to stop the outbreak have epidemiological advantages but also affected the personal, mental, and social and economic lives of many citizens.
“Gender is among the neglected social dimensions of the COVID-19 pandemic. From family to occupation up to the community level, gender plays a crucial role (sic),” he said.
Rabajante was tapped by the NRCP to develop the tool during the height of the enhanced community quarantine in Luzon in April, when research on the pandemic’s socio-economic dimensions was particularly wanting. His team took the challenge since there was a clear lack of focus on gender in local COVID-19 interventions.
An outcome of Rabajante’s “pure hard work,” the dashboard uses mainly data from the Department of Health (DOH)’s Data Drop and the Philippine Statistics Authority. Aside from its sex data filtering capability, it is replete with valuable features and publicly accessible online through a link (https://datastudio.google.com/s/rHHWJSPHmx4).
Anyone with a stable internet connection and a compatible device—a PC, laptop, or a tablet—can access and “play around” the dashboard’s datasets to render useful graphs and visualizations. The dashboard is regularly updated and maintained by Rabajante’s team upon the availability of new data from the DOH Data Drop. The team reports no breach in privacy being reliant on information that is publicly available.
Dashboard features, figures
The dashboard not only lets users break down datasets to render sex-disaggregated epidemic curve and graphs, it also allows them to easily crunch or manipulate data all at once by age group, health status (whether mild, asymptomatic, recovered, died), location (region, province, and municipal level), and a specific date range. In less than a minute, variables are colorfully rendered into modified epidemic curves and other charts that allow useful comparative analysis across the desired variables. The dashboard also includes data such as the number of pregnant patients from the number female patients, as well as data on repatriates.
Another unique feature are segregated pages for reports of COVID-19 cases based on: 1) the date of the onset of symptoms, 2) the date of collection of specimen, 3) the date of release of test result to patient, and 4) the final report publicly disseminated by the DOH. Such segregation, according to Rabajante, gives users a more complete picture or perspective on local COVID-19 reporting. He said using the specimen collection date is more helpful for analysis and planning—it is closest to but has more complete data than the onset of symptoms date and offsets expected delays in reporting.
He pointed out that the final DOH report, which issues the verified number of cases, recoveries, and deaths prefaced by a meticulous validation procedure, comes final in a sequence of stages. As of Tuesday, 7 July 2020, the dashboard computed the average lag at 13 days from swabbing date to specimen transfer, to processing and release of results, to public confirmation or announcement.
Rabajante also analyzes data from specimen collection date to derive the local time-varying reproduction number or “Rt,” which denotes the measure of contagiousness of a particular pathogen (like COVID-19) in a particular setting. Also, as of Tuesday, his team reported the country’s Rt at 1.5, which means that, on the average, every COVID-19 patient can infect 1.5 persons. A local Rt of 1.5 can result in an exponential increase in COVID-19 transmissions after some time.
“We don’t want Rt to exceed 1,” he said.
Further, Tuesday’s dashboard-computed average of six days from the day of onset of symptoms to specimen collection is a concern according to Rabajante since an individual may have already infected several others within that period. Moreover, it takes around 32 days on average for a patient to recover from COVID-19 upon the onset of symptoms. Deaths are publicly confirmed at over two weeks (19 days) on the average after a person dies of COVID-19, per the dashboard.
Dr. Jomar F. Rabajante, NRCP Governing Board Member-at-Large and UP Los Baños mathematics professor, guides webinar viewers on certain features of the dashboard remotely from his residence in Laguna, via Zoom webinar for KTOP-COVID by the NRCP on 7 July 2020. Per dashboard data, it takes around 32 days for a patient to recover from COVID-19 upon the onset of symptoms.
Sex-related differences, insights
Local vulnerability and risk factors include population size, population density, age distribution, and poverty incidence. Denser urbanized cities have greater outbreak risks than rural areas, consistent with the person-to-person mechanism of transmission. He proffered that poverty incidence is possibly inversely correlated with the other factors across the regions as economically poorer regions tend to have lower confirmed cases than more privileged ones. A majority of local transmission happens among the working segment of the population, and those working in the healthcare sector are at a higher risk for infection.
Across age groups, COVID-19 infects and kills more men than women in the country. However, males succumb at an average 12.44 days after onset of symptoms while females succumb at a shorter period of 10.99 days. Recovery duration from symptom onset is the same for both sexes.
Cited as well are women often being in the frontline, in health care centers, social services, communities, and households ensuring the well-being of family members. The following concerns were also raised relative to gender: the possibility of gender-based violence throughout the outbreak due to prolonged community quarantine; a perceived shift in gender roles in households due to the increase in number of unemployed individuals; and unintended pregnancies and sexually transmitted infections due to lessened access to basic reproductive health services because of lockdowns.
Meanwhile, despite the recent spike in COVID-19 cases in the country at 44,254 as of 5 July 2020, most confirmed cases are mild (about 92 percent) and asymptomatic (about seven percent), with relatively fewer severe and critical cases. Rabajante was quick to warn, however, that it is no reason for people to be complacent—a spike in COVID-19 cases increases the probability of infecting the vulnerable segments of the population, including older people (50 years and up) who are more at risk of dying due to the presence of comorbidities or pre-existing illnesses associated with age.
Dashboard graphs showing that most local cases are mild and asymptomatic. Right graph renders the same data in percentage, showing present cases are mostly mild (light blue), a great number of recoveries from March to May (pink), and decreasing trend in deaths (green), based on DOH-verified data. Dr. Rabajante, however, warns that the increase in cases also increases the risk of infecting more vulnerable groups. Link: https://datastudio.google.com/s/rHHWJSPHmx4
Marked improvement in testing, reporting
The data scientist also thanked the government for its improving data management and reporting when compared to its reporting back in March 2020, showing that it is paying attention to recommendations of local experts. The dashboard ascertains as well an increasing trend in the number of COVID-19 tests done locally.
The Council through Executive Director Marieta Bañez Sumagaysay thanked Dr. Rabajante and his team for responding to its call for gender-sensitivity in the analysis of COVID-19 data.
Although there are still reporting issues including limited sex-disaggregated variables from the DOH Data Drop, Dr. Rabajante is hopeful that the dashboard will be enhanced further to include other data such as occupation and comorbidities upon their addition in the DOH Data Drop.
The new local “gender-sensitive” COVID-19 dashboard is an output from the research project, “Gender-Specific Insights based on COVID-19 Epidemiological and Socio-Economic Data,” conducted within May 2020. Rabajanate’s co-researchers are Kyrell Vann Verano of the UP Open University Faculty of Education, and Sarah Mae Saipudin of the UPLB Gender Center. The initiative also received support from the UPLB Biomathematics Initiative and the UP System COVID-19 Pandemic Response Team.
Dr. Rabajante explains sex-disaggregated data from the dashboard.