Years ago, I spent several months painfully developing a mathematical prediction model to evaluate decreases in cervical cancer prevalence assuming introduction of papillomavirus vaccination in a developing country with different sexual mixing patterns1. The project required collaboration with virologists, social epidemiologists, and mathematicians as is often necessary when constructing epidemiological models reliant on biological, social, and statistical factors, among others. Along the way, I gained a strong appreciation for the complexity of these models, but also that every model depends upon assumptions laden with variability and uncertainty. Beginning as early as December 2019, reports surfaced about clinical cases in Wuhan, China of a new disease, later termed COVID-19, due to infection with a novel coronavirus, SARS-CoV-2. On March 11, 2020, the World Health Organization declared COVID-19 a pandemic2 at a time when there were 118,000 cases in 114 countries. Now, by May 28, 2020, there are over 5.8 million COVID-19 cases contributing to more than 360,000 deaths in 213 countries and territories3. In a scramble to understand the pandemic’s trends and trajectories, several epidemiological models have been developed aimed at predicting or forecasting COVID-19 cases, burden on the healthcare system, and overall mortality, given assumptions and available information. One might ask, why so many models producing different results? The aim of this paper is to summarize the key concepts and types of epidemiological models, why they are useful and limitations.
In response to the COVID-19 pandemic, many companies have taken steps to enable all of their employees to work from home. In addition to any existing home working procedures, further investments in to infrastructure and server capabilities should be considered, to ensure uninterrupted, effective home working for all employees, be that in a small company or a worldwide business such as PHASTAR’s. For example, PHASTAR’s IT department has provided additional support to staff through many avenues including VPN access, client system access and hardware in order to ensure that all staff can all provide the same level of support to our clients and their needs.
Whilst some employees may have been working from home for many years, it ought to be recognised that this will be a new experience for many others. From PHASTAR’s perspective, we are proud of the way that our team has risen to the challenge and continue to support clients without any interruptions to service or delivery – even recently completing a project ahead of schedule!
Clinical trial research is a fast-paced industry whereby the requirement to bring lifesaving and life changing drugs to market quickly, to meet patients needs, is a constant driver to looking for time saving innovations. As a result, pharmaceutical companies are frequently looking for ways to expedite clinical trial set up and execution while maintaining regulatory compliance and data quality.
At PHASTAR, we provide tailor made EDC solutions for our clients that will allow rapid set up and deployment, partnering with our EDC partners Medrio and Medidata to achieve this. During the contracting phase of a new study, importance is placed on actively engaging our partners and clients in exploring solutions for data capture that will ensure efficient database set-up, short timelines and quality data collection. For example, innovative and flexible forms to support long-term observational studies where data collection may vary between subjects depending on the local healthcare provider, ensuring flexibility while maintaining quality.
During a pandemic, data collection is of the upmost importance to be able to control and stop the spread of disease. It is essential to have adaptable, available data systems with clear, simplified forms that can be completed off-line and these systems must be operable both on and off network to enable field workers to complete the fields while working in remote locations and then later upload the data to a repository when there is network access. Overall, a solid IT system is necessary for quick capture and relay of information.
Another challenge during an outbreak response is creating and disseminating effective and timely information to the public. This requires a full understanding of the knowledge, attitudes and beliefs of the target audience. Target communication needs to account for the local level of understanding and literacy while being culturally appropriate. There needs to also be a way of monitoring if the communication is effective, again this needs to be done in real time. It is vital that communication is simple, consistent and reliable.
The current COVID-19 pandemic has seen a large increase of data systems and other forms of communication. Furthermore, there are a huge number of clinical studies ongoing or in the pipeline to address this crisis. Moving forward, a central repository housing essential information (e.g. trial protocols and analysis plans, interim findings, and real-time reporting of primary results) on completed and ongoing clinical trials will be needed, in order to encourage collaboration and information sharing, as well as to avoid any redundancy in research efforts.