Towards effective emerging infectious disease surveillance: H1N1 in the United States 1976 and Mexico 2009
Bibliographic record
Abstract
The comparison of Mexico’s 2009 A/H1N1 outbreak with the U.S. H1N1 outbreak of 1976 provides notable observations—based on the strengths and weaknesses of each country’s response—that can be used as a starting point of discussion for the design of effective Emerging Infectious Diseases (EIDs) surveillance programs in developing and middle-income countries.\n\nStrengths\nMexico’s strongest characteristics were its transparency, as well as the cooperation the country exhibited with other nations, particularly the U.S. and Canada. These were the result of Mexico’s existing professional relationships with other scientific communities—informal networks, existing without institutional ties, which proved highly beneficial.\n\nMexico also showed savvy in its effective management of public and media relations. By maintaining transparency and a united political front as it disseminated public health information, Mexico was able to mobilize in this area—something the U.S. handled less effectively in 1976. Uneven economic development was a barrier that prevented full dissemination across more rural regions of Mexico, but on a larger scale, public relations were handled relatively well.\n\nIn the U.S., the speed and efficiency of the 1976 U.S. mobilization against H1N1 was laudable. Although the U.S. response to the outbreak is seldom praised, the unity of the scientific and political communities demonstrated the national ability to respond to the situation. In parallel, Mexico also effectively responded to the situation, but in addition it had a preparedness plan for such a pandemic or bio-safety threat, which highlights the necessity of working out such strategies ahead of time.\n\nMexico’s effective pandemic-preparedness plan was comprehensive, but it was also based on simple issues: logistics, administrative structure, and information. The questions it answered included: Is there a national database on the cases of the virus at hand? Is there a network or panel of specialists that the government can pull to their aid? Who is maintaining this network? Are there designated transportation routes and potential central facilities to hold vaccines? Is there a designated individual who reviews the plan? \n\nWeaknesses\nIn the U.S., the major weakness was turning the response to the outbreak into a single go-or-no-go decision instead of splitting the decision into smaller action tasks or phases of implementation from which decisions could then be made. What made this situation more difficult was the unquestioning support of the Center for Disease Control’s (CDC) decision to execute a massive immunization campaign. While then President Ford and CDC Director David Sencer may have acted reasonably considering the circumstances, the move to immunize has since been much criticized, especially owing to the following rise in cases of Guillain-Barré Syndrome and the fact that H1N1 was never identified outside the Fort Dix, New Jersey, army base where it was first detected. \n\nIn Mexico, despite the country’s overall success in handling A/H1N1, there were myriad political weaknesses that hampered efforts, and these problems persist. Loyalty to political groups is prized above competence. In addition, individuals who are qualified for their position are perennially moved or must leave when there is a change in government, causing the loss of valuable institutional knowledge and relationships. These issues are hardly unique to Mexico, and will be especially important for countries developing EID surveillance tools to address in the coming years. \n\nAn even greater challenge for Mexico was an inflexible workplace culture that did not encourage workers to report abnormalities in patients and therefore delayed the identification of A/H1N1. Inefficiencies can be eliminated if laboratory employees are given the freedom to question situations and are provided with the hardware and tools for executing their duties. Worker compensation, relatively low for an Organization for Economic Cooperation and Development member country, could be an important factor as well.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".