Inaccuracy in disease surveillance will affect the goals of surveillance itself such as advocating interventions, monitoring of the impact of interventions, and control, elimination, and eradication of diseases.
When data overestimates the reality of a situation, it can lead to mobilizing resources or imposing social restrictions more than necessary, while underestimation can lead to prolonged interventions or a situation that is out of control such as the spread of infectious disease, requiring more efforts and resources.

The inaccurate data report will affect many aspects of the disease surveillance system, such as lack of exact incidence and prevalence of disease, misinterpretation of the burden of illness, outbreak pattern/magnitude/time trend/outlies/exposure, or incubation period the disease, etc. Therefore the inaccurate data report will lead to inappropriate measures to prevent and control the disease.
Data overestimate will affect two levels of public health resources. The first level is the individual case that the public health practitioner might have unnecessarily investigated or given prophylaxis treatment. The other level is the epidemic study which false-positive cases will lead to inappropriate trigger outbreak investigation.
Data underestimate will affect the individual health directly, which come from under-recognized this condition and might lead to delayed treatment. Moreover, data underestimate will reduce the chance of disease control, which usually needs control measures in a timely manner.

Inaccurate data reported to the surveillance system can generate wrong data that affect the following use of the data, such as planning for prevention, policymaking, and resources allocating to manage the problem.

Overestimated data can mislead the policymaking in focusing on the problem that does not exist, or should not be put in the first priority, leading to overlooking the real situation and spending too many resources on an unimportant issue

Underestimated data can leave the important problem undetected, untreated, and lastly, the problem might be too severe to handle easily.

Stakeholders will not know the size of the disease problem if the reported case is under-reported and no action is taken. if the case of over-reporting will have an impact on the accumulation of logistics that have been procured based on plans that use too much data

If the inaccurate data reported to the system, it affects the utilization of the data either overestimate or underestimate the real situation. If the data overestimate, the cost that was invested in surveillance system implementation may overabundant. Underestimate data makes that disease out of focus, be overlooked and does not have proper endorsement from the government. Moreover, unmatched data makes the disease preventive and control measures inaccurate and ineffective.

Policy-makers rely on available, reported data. These will guide decision-making and prioritization of resources for inclusion in health agenda.

Overestimating a health problem may result to:
– Wastage of resources (particularly financial resources) to address the problem
– Unnecessary panic among people residing in regions tagged with high prevalence/incidence where in fact, are doing well relative to others
– Unnecessary implementation of health protocols that may disrupt economic activities

Underestimating a health problem, on the other hand, may result to:
– an epidemic waiting to happen due to undetected cases
– misdiagnosed cases and eventually be treated for a wrong disease
– limited resources to address the problem

One way or another, inaccurate information leads to inaccurate conclusions and ultimately, wrong decisions.

Using inaccurate data can lead to various problems, related to the public health policy and management system for controlling and preventing the disease. In case of data overestimation, inappropriate increasing cost and resources to deal with the actual situation are encountered. On the contrary, utilizing underestimated data lead to delay detection and control of the disease, causing the disease to spread and increase morbidity and mortality. The increasing burden on time and resources to control and treatment of the disease that is out of control is really challenging when this situation occurs.

In the surveillance system, the inaccurate data collected will impact a proper diagnosis of the outbreak, analysis, and interpretation of disease and finally impede the control and prevention of the disease. As the data quality is also one of the evaluation indicators for the surveillance system it will affect the health outcome and mislead in the dissemination of the information. It also increases the cost and will not meet the objectives of the surveillance.

If the data does not represent the real situation, the authorities may mislead by such data and take incorrect ways to handle the disease. It is also costly. If the disease is overestimated, the authorities could spend more money than they needed to control the disease. On the other hand, if the disease is underestimated, it would be costly to treat the patients and to control the spread of the disease in the community.

Using inaccurate data can lead to public health problems. This will affect the hospital where the patient was admitted. Diseases that are hard to diagnose and treat usually have high costs. The hospital will report for lower deduction DRGs. It resulted in financial problems and incorrect surveillance reports. If inaccurate surveillance reports were sent to the Bureau of Epidemiology. Underestimate surveillance data will lead to pandemics of the disease. Overestimate surveillance data will result in wasting cost and workforce.

Inaccurate data reported to the surveillance system could lead to incorrect data either underestimating or overestimating the prevalence of diseases. It can lead to public health problems, resource distribution planning, wrong decision-making, and untruthful epidemiological data. For example, with underestimated incidence/prevalence, the organization may be likely to preserve fewer resources (budget/workforce) than the actual situation which could lead to ineffective disease prevention and control plans. Overestimated data will affect on the financial planning and waste resources.

Inaccurate data can consequently cause impactful problems.
– Inaccurate data lead to underestimated conclusion that might cause delayed actions and lead to tragedy. On the other hand, Overestimated conclusion caused by inaccurate data might lead to overwhelming action or too strict policy.
– Inaccurate data lead to misunderstanding in aspect of pathology, epidemiology, treatment, and prevention that can cause big problems later.

The surveillance system which providing inaccurate data can be abysmal. When the data is over-recognition, it may cause the false alarm which require the immediate action to stop the false outbreak. This result in wasting the workforce and fund to take action on the false outbreak, as well as spreading the panic through the outbreak disclosure. Underestimated data unimaginably affects the real outbreak situation because it is not able to detect the outbreak, which delays the outbreak control/handling.

Inaccurate data reported to surveillance systems lead to misunderstanding in epidemiology, delay in detection and prevemtion. On the other hand if the data was overestimated reported, it will be wastage of resources .