.tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wpv-view-output[data-toolset-views-view-editor="55c7fd30fd3849e31c9d22e9653ceaf4"] > .tb-grid-column:nth-of-type(3n + 1) { grid-column: 1 } .wpv-view-output[data-toolset-views-view-editor="55c7fd30fd3849e31c9d22e9653ceaf4"] > .tb-grid-column:nth-of-type(3n + 2) { grid-column: 2 } .wpv-view-output[data-toolset-views-view-editor="55c7fd30fd3849e31c9d22e9653ceaf4"] > .tb-grid-column:nth-of-type(3n + 3) { grid-column: 3 } .wpv-view-output[data-toolset-views-view-editor="55c7fd30fd3849e31c9d22e9653ceaf4"] .js-wpv-loop-wrapper > .tb-grid { grid-template-columns: minmax(0, 0.3333fr) minmax(0, 0.3333fr) minmax(0, 0.3333fr);grid-auto-flow: row } .tb-container .tb-container-inner{width:100%;margin:0 auto} .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="751d72269a9fca740ed3ffe1ce3a8869"] { padding: 0px; }  .tb-image{position:relative;transition:transform 0.25s ease}.wp-block-image .tb-image.aligncenter{margin-left:auto;margin-right:auto}.tb-image img{max-width:100%;height:auto;width:auto;transition:transform 0.25s ease}.tb-image .tb-image-caption-fit-to-image{display:table}.tb-image .tb-image-caption-fit-to-image .tb-image-caption{display:table-caption;caption-side:bottom} .wp-block-image.tb-image[data-toolset-blocks-image="fa391a7576a4dc75b41dcfee79f00759"] { max-width: 100%; } .wp-block-image.tb-image[data-toolset-blocks-image="fa391a7576a4dc75b41dcfee79f00759"] img { padding: 0px;margin: 0px; }  .tb-container .tb-container-inner{width:100%;margin:0 auto} .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="76b9e19aebd78b467b04c64acfe33167"] { padding: 0px; } .tb-social-share__network{display:inline-block;text-align:center;vertical-align:top;margin-right:7px;margin-bottom:7px}.tb-social-share--092 .tb-social-share__facebook__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#3b5998;}.tb-social-share--092 .tb-social-share__linkedin__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#007fb1;}.tb-social-share--092 .tb-social-share__twitter__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#00aced;}.tb-social-share--092 .tb-social-share__pinterest__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#cb2128;}.tb-social-share--092 .tb-social-share__telegram__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#37aee2;}.tb-social-share--092 .tb-social-share__reddit__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#5f99cf;}.tb-social-share--092 .tb-social-share__viber__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#7c529e;}.tb-social-share--092 .tb-social-share__email__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#7f7f7f;}.tb-social-share--round .SocialMediaShareButton{border-radius:50%}.tb-social-share__excerpt{display:none}.tb-social-share .SocialMediaShareButton--disabled{opacity:0.65} .tb-social-share[data-toolset-blocks-social-share="5e0e5387fa9ff0ed5c08d011829ff87a"] .SocialMediaShareButton { width: 32px;height: 32px; } .tb-fields-and-text[data-toolset-blocks-fields-and-text="0c96bd04aebe765eed2954a3bfddb0f1"] { color: rgba( 0, 0, 0, 1 ); } .tb-fields-and-text[data-toolset-blocks-fields-and-text="0c96bd04aebe765eed2954a3bfddb0f1"] p { color: rgba( 0, 0, 0, 1 ); }  @media only screen and (max-width: 781px) { .tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wpv-view-output[data-toolset-views-view-editor="55c7fd30fd3849e31c9d22e9653ceaf4"] > .tb-grid-column:nth-of-type(2n + 1) { grid-column: 1 } .wpv-view-output[data-toolset-views-view-editor="55c7fd30fd3849e31c9d22e9653ceaf4"] > .tb-grid-column:nth-of-type(2n + 2) { grid-column: 2 } .wpv-view-output[data-toolset-views-view-editor="55c7fd30fd3849e31c9d22e9653ceaf4"] .js-wpv-loop-wrapper > .tb-grid { grid-template-columns: minmax(0, 0.5fr) minmax(0, 0.5fr);grid-auto-flow: row } .tb-container .tb-container-inner{width:100%;margin:0 auto} .wp-block-toolset-blocks-container.tb-container[data-toolset-blocks-container="751d72269a9fca740ed3ffe1ce3a8869"] { padding: 0px;margin: 0px; }  .tb-image{position:relative;transition:transform 0.25s ease}.wp-block-image .tb-image.aligncenter{margin-left:auto;margin-right:auto}.tb-image img{max-width:100%;height:auto;width:auto;transition:transform 0.25s ease}.tb-image .tb-image-caption-fit-to-image{display:table}.tb-image .tb-image-caption-fit-to-image .tb-image-caption{display:table-caption;caption-side:bottom} .wp-block-image.tb-image[data-toolset-blocks-image="fa391a7576a4dc75b41dcfee79f00759"] img { padding: 0px;margin-top: 0px;margin-right: 0px;margin-left: 0px; }  .tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-social-share__network{display:inline-block;text-align:center;vertical-align:top;margin-right:7px;margin-bottom:7px}.tb-social-share--092 .tb-social-share__facebook__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#3b5998;}.tb-social-share--092 .tb-social-share__linkedin__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#007fb1;}.tb-social-share--092 .tb-social-share__twitter__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#00aced;}.tb-social-share--092 .tb-social-share__pinterest__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#cb2128;}.tb-social-share--092 .tb-social-share__telegram__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#37aee2;}.tb-social-share--092 .tb-social-share__reddit__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#5f99cf;}.tb-social-share--092 .tb-social-share__viber__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#7c529e;}.tb-social-share--092 .tb-social-share__email__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#7f7f7f;}.tb-social-share--round .SocialMediaShareButton{border-radius:50%}.tb-social-share__excerpt{display:none}.tb-social-share .SocialMediaShareButton--disabled{opacity:0.65}  } @media only screen and (max-width: 599px) { .tb-grid,.tb-grid>.block-editor-inner-blocks>.block-editor-block-list__layout{display:grid;grid-row-gap:25px;grid-column-gap:25px}.tb-grid-item{background:#d38a03;padding:30px}.tb-grid-column{flex-wrap:wrap}.tb-grid-column>*{width:100%}.tb-grid-column.tb-grid-align-top{width:100%;display:flex;align-content:flex-start}.tb-grid-column.tb-grid-align-center{width:100%;display:flex;align-content:center}.tb-grid-column.tb-grid-align-bottom{width:100%;display:flex;align-content:flex-end} .wpv-view-output[data-toolset-views-view-editor="55c7fd30fd3849e31c9d22e9653ceaf4"]  > .tb-grid-column:nth-of-type(1n+1) { grid-column: 1 } .wpv-view-output[data-toolset-views-view-editor="55c7fd30fd3849e31c9d22e9653ceaf4"] .js-wpv-loop-wrapper > .tb-grid { grid-template-columns: minmax(0, 1fr);grid-auto-flow: row } .tb-container .tb-container-inner{width:100%;margin:0 auto} .tb-image{position:relative;transition:transform 0.25s ease}.wp-block-image .tb-image.aligncenter{margin-left:auto;margin-right:auto}.tb-image img{max-width:100%;height:auto;width:auto;transition:transform 0.25s ease}.tb-image .tb-image-caption-fit-to-image{display:table}.tb-image .tb-image-caption-fit-to-image .tb-image-caption{display:table-caption;caption-side:bottom} .tb-container .tb-container-inner{width:100%;margin:0 auto}.tb-social-share__network{display:inline-block;text-align:center;vertical-align:top;margin-right:7px;margin-bottom:7px}.tb-social-share--092 .tb-social-share__facebook__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#3b5998;}.tb-social-share--092 .tb-social-share__linkedin__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#007fb1;}.tb-social-share--092 .tb-social-share__twitter__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#00aced;}.tb-social-share--092 .tb-social-share__pinterest__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#cb2128;}.tb-social-share--092 .tb-social-share__telegram__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#37aee2;}.tb-social-share--092 .tb-social-share__reddit__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#5f99cf;}.tb-social-share--092 .tb-social-share__viber__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#7c529e;}.tb-social-share--092 .tb-social-share__email__share-button{cursor:pointer;display:inline-block;background-size:contain;background-color:#7f7f7f;}.tb-social-share--round .SocialMediaShareButton{border-radius:50%}.tb-social-share__excerpt{display:none}.tb-social-share .SocialMediaShareButton--disabled{opacity:0.65}.tb-fields-and-text[data-toolset-blocks-fields-and-text="0c96bd04aebe765eed2954a3bfddb0f1"] { font-size: 18px; } .tb-fields-and-text[data-toolset-blocks-fields-and-text="0c96bd04aebe765eed2954a3bfddb0f1"] p { font-size: 18px; }   } 

What can previous outbreak modeling tell us about the COVID-19 pandemic?

pharmafile | May 11, 2020 | Feature | Business Services, Manufacturing and Production, Medical Communications, Research and Development, Sales and Marketing | COVID-19, Interview, coronavirus, feature, pharma

Dr LaRee Tracy, a lecturer at San Diego State University and Director of Biostatistics at PHASTAR, led a team of statisticians in utilising data modeling to track and respond to West Africa’s devastating Ebola virus epidemic in 2014. She discusses the lessons learned from the outbreak, and how they can help shape our response to the current coronavirus pandemic.

Can explain exactly how you and your team synced different forms of data to follow and predict the spread of the 2014 Ebola outbreak?

Data were often not linked due to lack of a unique ID assigned to persons with confirmed or suspected Ebola infection. Clinical and laboratory data were linkable after linking IDs; however, linking each case with their contacts and tracing contacts back to the original case was difficult. We attempted to create a data searching program to attempt to link cases with contacts, but it often did not match enough cases. This created a major challenge in tracking and monitoring the disease, particularly in attempting to understand how new cases were occurring, i.e. contacts from prior cases or some new cluster of infections.

What lessons did you learn from tackling the outbreak in monitoring new infectious diseases? How can that knowledge be applied to the COVID-19 outbreak?

During an outbreak, data collection is paramount to be able to control and stop disease spread. Having adaptable, deployable data systems with clear, simplified forms that can be completed off-line is essential. 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 solid understanding of 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 witnessed an explosion of data systems and other forms of communication. Also, there is a large number of clinical studies ongoing or planned to address this crisis. Moving forward, a central repository housing essential information (for example, trial protocols and analysis plans, interim findings, and real-time reporting of primary results) on completed and ongoing clinical trials is needed to promote information sharing and to avoid redundancy in research efforts.

Is it possible to follow and predict the spread of COVID-19’s outbreak using the same methodology? If so, based on this data, what do you think are the most imperative steps to take in confronting the pandemic?

Real-time data sharing of not only case counts and mortality figures but of also patient-level characteristics and demographics is needed. This disease is affecting a widely heterogeneous population and outcomes are variable. To better understand the epidemiology of this disease, more information on patient classification and comorbidity are required.

How can we consolidate the best available data and science to ensure that the most accurate picture of the outbreak is put together, and to ensure all nations have access to the best preventative and therapeutic guidance?

A central site housing all ongoing, completed and planned clinical trials including protocols, analysis plans and primary findings is needed. An organisation, e.g. WHO, NIH, Bill & Melinda Gates Foundation, etc. could sponsor this site with funding and support from private industry. Information provided on the site could be decided, in real time, by an independent panel of scientists and entrepreneurs. Challenges might include reluctance of companies to make public trial protocols, analysis plans, and results.

What challenges does an outbreak like this present to our capacity for designing, setting up and running clinical trials?

The biggest challenge is the rush to use experimental or off-label treatments in the absence of evidence proving safety and efficacy from randomised clinical trials (RCTs). This is happening now in that single arm studies and/or case series data are made public, leading to use of products prior to those products demonstrating safety and efficacy in well-controlled RCTs, which is the only design that can lead to conclusions of safety and efficacy.

What do you think should be done to address these issues and make this process more effective?

Promote well-designed, large, global, RCTs with clear and specific endpoints. These trials could be funded and sponsored by large research groups or donor groups, e.g. the Bill & Melinda Gates Foundation, etc. Large RCTs performed under a standard, master protocol in which all trials follow the same or similar design would lead to interpretable information. Prior experiences of conducting RCTs under a master protocol include the 2014-2015 West Africa Ebola outbreak (PREVAIL II trial, NCT02344407) and more recently during the Ebola outbreak in the Democratic Republic of the Congo (PALM trial, NCT03719586).

What are the challenges specifically in trialing a vaccine for a brand new strain of virus? How can we meet these challenges?

Experience from other coronavirus strains may be informative when developing a vaccine to prevent SARS-COV-2 infection. Challenges in developing a new vaccine include the need for exceptional safety given that the intended indication is for prevention in a healthy population. Also, a trial will need to rule out/exclude persons who have already been infected and therefore there needs to be a way to identify persons with current or prior infection. Presently, only molecular testing to identify current infection is available. Serology testing to identify presence of antibodies has yet to be validated.

The EMA has called on the member states of the European Union to collaborate on the clinical trials for COVID-19 treatments. They have been urged to prioritise larger multi-country RCTs. What is the benefit of this over other styles of clinical trial?

Yes, large, global, adequate and well-controlled clinical trials will yield the most robust and timely information during this outbreak. Such trials could be designed to include interim analyses to evaluate early for futility and efficacy and could be adaptive to add or drop treatment arms given pre-specific stopping rules.

Broadly, do you think world nations are following the available science effectively enough on how best to minimise the spread and damage of this outbreak?

In the US, the response was a bit slow given that there were reported cases as far back as December 2019, as well as delays in implementing necessary testing. Science should certainly drive the decisions as to when and how to allow people to return to work and increase community activity. The necessary information for this includes improved monitoring and reporting of infected persons and their contacts at the local level. A one-size-fits all approach will not likely apply in the US given the variability in disease across states. Once a validated serological test is made available, random sampling based on population size, geography and movement is necessary to gain a sense as the level of immune/semi-immune persons in the community. These data will help inform as to how and when to allow a return to normal activity.